# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/f80d4d8b77c53435e9c0a9045636f1bfb2b8c539/chart/deployed_strategies/binance-michael-k8s-namespace/NostalgiaForInfinityNext_maximizer.py import copy import logging import pathlib import rapidjson import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.misc import json_load, file_dump_json from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes from freqtrade.strategy import DecimalParameter, IntParameter, CategoricalParameter from freqtrade.exchange import timeframe_to_prev_date from pandas import DataFrame, Series, concat from functools import reduce import math from freqtrade.persistence import Trade from datetime import datetime, timedelta from technical.util import resample_to_interval, resampled_merge from technical.indicators import zema, VIDYA, ichimoku import pandas_ta as pta import os import json from typing import Dict log = logging.getLogger(__name__) ########################################################################################################### ## NostalgiaForInfinityV8 by iterativ ## ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ## ## A pairlist with 40 to 80 pairs. Volume pairlist works well. ## ## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ## ## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ## ## Ensure that you don't override any variables in you config.json. Especially ## ## the timeframe (must be 5m). ## ## use_exit_signal must set to true (or not set at all). ## ## exit_profit_only must set to false (or not set at all). ## ## ignore_roi_if_entry_signal must set to true (or not set at all). ## ## ## ########################################################################################################### ## HOLD SUPPORT ## ## In case you want to have SOME of the trades to only be sold when on profit, add a file named ## ## "hold-trades.json" in the same directory as this strategy. ## ## ## ## The contents should be similar to: ## ## ## ## {"trade_ids": [1, 3, 7], "profit_ratio": 0.005} ## ## ## ## Or, for individual profit ratios(Notice the trade ID's as strings: ## ## ## ## {"trade_ids": {"1": 0.001, "3": -0.005, "7": 0.05}} ## ## ## ## NOTE: ## ## * `trade_ids` is a list of integers, the trade ID's, which you can get from the logs or from the ## ## output of the telegram status command. ## ## * Regardless of the defined profit ratio(s), the strategy MUST still produce a SELL signal for the ## ## HOLD support logic to run ## ## ## ########################################################################################################### ## DONATIONS ## ## ## ## Absolutely not required. However, will be accepted as a token of appreciation. ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH (ERC20): 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## BEP20/BSC (ETH, BNB, ...): 0x86A0B21a20b39d16424B7c8003E4A7e12d78ABEe ## ## ## ########################################################################################################### class Github_DerSalvador_freqtrade_helm_chart__NostalgiaForInfinityNext_maximizer__20260416_224245(IStrategy): INTERFACE_VERSION = 3 plot_config = {'main_plot': {}, 'subplots': {'entry tag': {'entry_tag': {'color': 'green'}}}} # ROI table: minimal_roi = {'0': 10} stoploss = -0.99 # Initialize custom_info custom_info = {} # Trailing stoploss (not used) trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 use_custom_stoploss = False # Optimal timeframe for the strategy. timeframe = '5m' res_timeframe = 'none' info_timeframe = '1h' # BTC informative has_BTC_base_tf = False has_BTC_info_tf = True # Backtest Age Filter emulation has_bt_agefilter = False bt_min_age_days = 3 # Exchange Downtime protection has_downtime_protection = False # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the "ask_strategy" section in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 480 # Optional order type mapping. order_types = {'entry': 'limit', 'exit': 'limit', 'trailing_stop_loss': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False} ############################################################# ############# # Enable/Disable conditions ############# entry_params = {'entry_condition_1_enable': True, 'entry_condition_2_enable': True, 'entry_condition_3_enable': True, 'entry_condition_4_enable': True, 'entry_condition_5_enable': True, 'entry_condition_6_enable': True, 'entry_condition_7_enable': True, 'entry_condition_8_enable': True, 'entry_condition_9_enable': True, 'entry_condition_10_enable': True, 'entry_condition_11_enable': True, 'entry_condition_12_enable': True, 'entry_condition_13_enable': True, 'entry_condition_14_enable': True, 'entry_condition_15_enable': True, 'entry_condition_16_enable': True, 'entry_condition_17_enable': True, 'entry_condition_18_enable': True, 'entry_condition_19_enable': True, 'entry_condition_20_enable': True, 'entry_condition_21_enable': True, 'entry_condition_22_enable': True, 'entry_condition_23_enable': True, 'entry_condition_24_enable': True, 'entry_condition_25_enable': True, 'entry_condition_26_enable': True, 'entry_condition_27_enable': True, 'entry_condition_28_enable': True, 'entry_condition_29_enable': True, 'entry_condition_30_enable': True, 'entry_condition_31_enable': True, 'entry_condition_32_enable': True, 'entry_condition_33_enable': True, 'entry_condition_34_enable': True, 'entry_condition_35_enable': True, 'entry_condition_36_enable': True, 'entry_condition_37_enable': True, 'entry_condition_38_enable': True, 'entry_condition_39_enable': True, 'entry_condition_40_enable': True, 'entry_condition_41_enable': True, 'entry_condition_42_enable': True, 'entry_condition_43_enable': True} ############# # Enable/Disable conditions ############# exit_params = {'exit_condition_1_enable': True, 'exit_condition_2_enable': True, 'exit_condition_3_enable': True, 'exit_condition_4_enable': True, 'exit_condition_5_enable': True, 'exit_condition_6_enable': True, 'exit_condition_7_enable': True, 'exit_condition_8_enable': True} ############# # Enable/Disable conditions ############# profit_target_params = {'profit_target_1_enable': True} ############################################################# entry_protection_params = {1: {'ema_fast': False, 'ema_fast_len': '26', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': True, 'sma200_rising_val': '28', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '80', 'safe_pump': False, 'safe_pump_type': '70', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 2: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '20', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 3: {'ema_fast': True, 'ema_fast_len': '100', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '70', 'safe_pump': True, 'safe_pump_type': '100', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 4: {'ema_fast': True, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': True, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '110', 'safe_pump_period': '48', 'btc_1h_not_downtrend': False}, 5: {'ema_fast': True, 'ema_fast_len': '100', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '100', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '30', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 6: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '50', 'safe_pump': True, 'safe_pump_type': '20', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 7: {'ema_fast': True, 'ema_fast_len': '100', 'ema_slow': True, 'ema_slow_len': '12', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': True, 'safe_pump_type': '120', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 8: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '12', 'close_above_ema_fast': True, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '120', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 9: {'ema_fast': True, 'ema_fast_len': '100', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '10', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 10: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '24', 'safe_dips': True, 'safe_dips_type': '120', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 11: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 12: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': True, 'sma200_1h_rising_val': '24', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': True, 'safe_pump_type': '40', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 13: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': True, 'sma200_1h_rising_val': '24', 'safe_dips': True, 'safe_dips_type': '20', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 14: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': True, 'sma200_rising_val': '30', 'sma200_1h_rising': True, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '120', 'safe_pump': False, 'safe_pump_type': '100', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 15: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': True, 'safe_pump_type': '20', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 16: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '50', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '10', 'safe_pump': True, 'safe_pump_type': '10', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 17: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '120', 'safe_pump': True, 'safe_pump_type': '120', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 18: {'ema_fast': True, 'ema_fast_len': '100', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': True, 'close_above_ema_slow_len': '200', 'sma200_rising': True, 'sma200_rising_val': '44', 'sma200_1h_rising': True, 'sma200_1h_rising_val': '72', 'safe_dips': True, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '120', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 19: {'ema_fast': True, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '36', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 20: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '10', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 21: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '90', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 22: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': True, 'safe_pump_type': '110', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 23: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': True, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': True, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 24: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': True, 'sma200_1h_rising_val': '36', 'safe_dips': True, 'safe_dips_type': '20', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 25: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': True, 'sma200_rising_val': '20', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '10', 'safe_pump': True, 'safe_pump_type': '20', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 26: {'ema_fast': False, 'ema_fast_len': '100', 'ema_slow': True, 'ema_slow_len': '12', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '10', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '36', 'btc_1h_not_downtrend': True}, 27: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '36', 'btc_1h_not_downtrend': True}, 28: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '50', 'safe_pump': True, 'safe_pump_type': '110', 'safe_pump_period': '36', 'btc_1h_not_downtrend': True}, 29: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '110', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 30: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '110', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 31: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '110', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '48', 'btc_1h_not_downtrend': False}, 32: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '120', 'safe_pump': True, 'safe_pump_type': '120', 'safe_pump_period': '48', 'btc_1h_not_downtrend': False}, 33: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '10', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 34: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 35: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 36: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 37: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '100', 'safe_pump_period': '48', 'btc_1h_not_downtrend': False}, 38: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '36', 'btc_1h_not_downtrend': True}, 39: {'ema_fast': False, 'ema_fast_len': '100', 'ema_slow': True, 'ema_slow_len': '15', 'close_above_ema_fast': True, 'close_above_ema_fast_len': '100', 'close_above_ema_slow': True, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '48', 'btc_1h_not_downtrend': True}, 40: {'ema_fast': True, 'ema_fast_len': '12', 'ema_slow': True, 'ema_slow_len': '25', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': True, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': False, 'safe_dips_type': '130', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '48', 'btc_1h_not_downtrend': True}, 41: {'ema_fast': False, 'ema_fast_len': '12', 'ema_slow': True, 'ema_slow_len': '12', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': True, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '120', 'safe_pump_period': '24', 'btc_1h_not_downtrend': True}, 42: {'ema_fast': False, 'ema_fast_len': '12', 'ema_slow': False, 'ema_slow_len': '12', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': True, 'safe_dips_type': '110', 'safe_pump': False, 'safe_pump_type': '100', 'safe_pump_period': '24', 'btc_1h_not_downtrend': True}, 43: {'ema_fast': False, 'ema_fast_len': '12', 'ema_slow': False, 'ema_slow_len': '12', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': True, 'safe_dips_type': '70', 'safe_pump': False, 'safe_pump_type': '100', 'safe_pump_period': '24', 'btc_1h_not_downtrend': True}} # Strict dips - level 10 entry_dip_threshold_10_1 = 0.015 entry_dip_threshold_10_2 = 0.1 entry_dip_threshold_10_3 = 0.24 entry_dip_threshold_10_4 = 0.42 # Strict dips - level 20 entry_dip_threshold_20_1 = 0.016 entry_dip_threshold_20_2 = 0.11 entry_dip_threshold_20_3 = 0.26 entry_dip_threshold_20_4 = 0.44 # Strict dips - level 30 entry_dip_threshold_30_1 = 0.018 entry_dip_threshold_30_2 = 0.12 entry_dip_threshold_30_3 = 0.28 entry_dip_threshold_30_4 = 0.46 # Strict dips - level 40 entry_dip_threshold_40_1 = 0.019 entry_dip_threshold_40_2 = 0.13 entry_dip_threshold_40_3 = 0.3 entry_dip_threshold_40_4 = 0.48 # Normal dips - level 50 entry_dip_threshold_50_1 = 0.02 entry_dip_threshold_50_2 = 0.14 entry_dip_threshold_50_3 = 0.32 entry_dip_threshold_50_4 = 0.5 # Normal dips - level 60 entry_dip_threshold_60_1 = 0.022 entry_dip_threshold_60_2 = 0.18 entry_dip_threshold_60_3 = 0.34 entry_dip_threshold_60_4 = 0.56 # Normal dips - level 70 entry_dip_threshold_70_1 = 0.023 entry_dip_threshold_70_2 = 0.2 entry_dip_threshold_70_3 = 0.36 entry_dip_threshold_70_4 = 0.6 # Normal dips - level 80 entry_dip_threshold_80_1 = 0.024 entry_dip_threshold_80_2 = 0.22 entry_dip_threshold_80_3 = 0.38 entry_dip_threshold_80_4 = 0.66 # Normal dips - level 70 entry_dip_threshold_90_1 = 0.025 entry_dip_threshold_90_2 = 0.23 entry_dip_threshold_90_3 = 0.4 entry_dip_threshold_90_4 = 0.7 # Loose dips - level 100 entry_dip_threshold_100_1 = 0.026 entry_dip_threshold_100_2 = 0.24 entry_dip_threshold_100_3 = 0.42 entry_dip_threshold_100_4 = 0.8 # Loose dips - level 110 entry_dip_threshold_110_1 = 0.027 entry_dip_threshold_110_2 = 0.26 entry_dip_threshold_110_3 = 0.44 entry_dip_threshold_110_4 = 0.84 # Loose dips - level 120 entry_dip_threshold_120_1 = 0.028 entry_dip_threshold_120_2 = 0.28 entry_dip_threshold_120_3 = 0.46 entry_dip_threshold_120_4 = 0.86 # Loose dips - level 130 entry_dip_threshold_130_1 = 0.028 entry_dip_threshold_130_2 = 0.3 entry_dip_threshold_130_3 = 0.48 entry_dip_threshold_130_4 = 0.9 # 24 hours - level 10 entry_pump_pull_threshold_10_24 = 2.2 entry_pump_threshold_10_24 = 0.42 # 36 hours - level 10 entry_pump_pull_threshold_10_36 = 2.0 entry_pump_threshold_10_36 = 0.58 # 48 hours - level 10 entry_pump_pull_threshold_10_48 = 2.0 entry_pump_threshold_10_48 = 0.8 # 24 hours - level 20 entry_pump_pull_threshold_20_24 = 2.2 entry_pump_threshold_20_24 = 0.46 # 36 hours - level 20 entry_pump_pull_threshold_20_36 = 2.0 entry_pump_threshold_20_36 = 0.6 # 48 hours - level 20 entry_pump_pull_threshold_20_48 = 2.0 entry_pump_threshold_20_48 = 0.81 # 24 hours - level 30 entry_pump_pull_threshold_30_24 = 2.2 entry_pump_threshold_30_24 = 0.5 # 36 hours - level 30 entry_pump_pull_threshold_30_36 = 2.0 entry_pump_threshold_30_36 = 0.62 # 48 hours - level 30 entry_pump_pull_threshold_30_48 = 2.0 entry_pump_threshold_30_48 = 0.82 # 24 hours - level 40 entry_pump_pull_threshold_40_24 = 2.2 entry_pump_threshold_40_24 = 0.54 # 36 hours - level 40 entry_pump_pull_threshold_40_36 = 2.0 entry_pump_threshold_40_36 = 0.63 # 48 hours - level 40 entry_pump_pull_threshold_40_48 = 2.0 entry_pump_threshold_40_48 = 0.84 # 24 hours - level 50 entry_pump_pull_threshold_50_24 = 1.75 entry_pump_threshold_50_24 = 0.6 # 36 hours - level 50 entry_pump_pull_threshold_50_36 = 1.75 entry_pump_threshold_50_36 = 0.64 # 48 hours - level 50 entry_pump_pull_threshold_50_48 = 1.75 entry_pump_threshold_50_48 = 0.85 # 24 hours - level 60 entry_pump_pull_threshold_60_24 = 1.75 entry_pump_threshold_60_24 = 0.62 # 36 hours - level 60 entry_pump_pull_threshold_60_36 = 1.75 entry_pump_threshold_60_36 = 0.66 # 48 hours - level 60 entry_pump_pull_threshold_60_48 = 1.75 entry_pump_threshold_60_48 = 0.9 # 24 hours - level 70 entry_pump_pull_threshold_70_24 = 1.75 entry_pump_threshold_70_24 = 0.63 # 36 hours - level 70 entry_pump_pull_threshold_70_36 = 1.75 entry_pump_threshold_70_36 = 0.67 # 48 hours - level 70 entry_pump_pull_threshold_70_48 = 1.75 entry_pump_threshold_70_48 = 0.95 # 24 hours - level 80 entry_pump_pull_threshold_80_24 = 1.75 entry_pump_threshold_80_24 = 0.64 # 36 hours - level 80 entry_pump_pull_threshold_80_36 = 1.75 entry_pump_threshold_80_36 = 0.68 # 48 hours - level 80 entry_pump_pull_threshold_80_48 = 1.75 entry_pump_threshold_80_48 = 1.0 # 24 hours - level 90 entry_pump_pull_threshold_90_24 = 1.75 entry_pump_threshold_90_24 = 0.65 # 36 hours - level 90 entry_pump_pull_threshold_90_36 = 1.75 entry_pump_threshold_90_36 = 0.69 # 48 hours - level 90 entry_pump_pull_threshold_90_48 = 1.75 entry_pump_threshold_90_48 = 1.1 # 24 hours - level 100 entry_pump_pull_threshold_100_24 = 1.7 entry_pump_threshold_100_24 = 0.66 # 36 hours - level 100 entry_pump_pull_threshold_100_36 = 1.7 entry_pump_threshold_100_36 = 0.7 # 48 hours - level 100 entry_pump_pull_threshold_100_48 = 1.4 entry_pump_threshold_100_48 = 1.6 # 24 hours - level 110 entry_pump_pull_threshold_110_24 = 1.7 entry_pump_threshold_110_24 = 0.7 # 36 hours - level 110 entry_pump_pull_threshold_110_36 = 1.7 entry_pump_threshold_110_36 = 0.74 # 48 hours - level 110 entry_pump_pull_threshold_110_48 = 1.4 entry_pump_threshold_110_48 = 1.8 # 24 hours - level 120 entry_pump_pull_threshold_120_24 = 1.7 entry_pump_threshold_120_24 = 0.78 # 36 hours - level 120 entry_pump_pull_threshold_120_36 = 1.7 entry_pump_threshold_120_36 = 0.78 # 48 hours - level 120 entry_pump_pull_threshold_120_48 = 1.4 entry_pump_threshold_120_48 = 2.0 # 5 hours - level 10 entry_dump_protection_10_5 = 0.4 # 5 hours - level 20 entry_dump_protection_20_5 = 0.44 # 5 hours - level 30 entry_dump_protection_30_5 = 0.5 # 5 hours - level 40 entry_dump_protection_40_5 = 0.58 # 5 hours - level 50 entry_dump_protection_50_5 = 0.66 # 5 hours - level 60 entry_dump_protection_60_5 = 0.74 entry_min_inc_1 = 0.022 entry_rsi_1h_min_1 = 20.0 entry_rsi_1h_max_1 = 84.0 entry_rsi_1 = 36.0 entry_mfi_1 = 50.0 entry_cti_1 = -0.92 entry_rsi_1h_min_2 = 32.0 entry_rsi_1h_max_2 = 84.0 entry_rsi_1h_diff_2 = 38.8 entry_mfi_2 = 49.0 entry_bb_offset_2 = 0.983 entry_volume_2 = 1.6 entry_bb40_bbdelta_close_3 = 0.045 entry_bb40_closedelta_close_3 = 0.023 entry_bb40_tail_bbdelta_3 = 0.418 entry_ema_rel_3 = 0.986 entry_cti_3 = -0.5 entry_bb20_close_bblowerband_4 = 0.979 entry_bb20_volume_4 = 10.0 entry_cti_4 = -0.8 entry_ema_open_mult_5 = 0.018 entry_bb_offset_5 = 0.996 entry_ema_rel_5 = 0.915 entry_cti_5 = -0.84 entry_volume_5 = 1.8 entry_ema_open_mult_6 = 0.021 entry_bb_offset_6 = 0.976 entry_ema_open_mult_7 = 0.03 entry_cti_7 = -0.89 entry_cti_8 = -0.88 entry_rsi_8 = 40.0 entry_bb_offset_8 = 0.99 entry_rsi_1h_8 = 64.0 entry_volume_8 = 1.8 entry_ma_offset_9 = 0.968 entry_bb_offset_9 = 0.942 entry_rsi_1h_min_9 = 20.0 entry_rsi_1h_max_9 = 88.0 entry_mfi_9 = 50.0 entry_ma_offset_10 = 0.98 entry_bb_offset_10 = 0.972 entry_rsi_1h_10 = 50.0 entry_ma_offset_11 = 0.946 entry_min_inc_11 = 0.038 entry_rsi_1h_min_11 = 46.0 entry_rsi_1h_max_11 = 84.0 entry_rsi_11 = 38.0 entry_mfi_11 = 36.0 entry_ma_offset_12 = 0.921 entry_rsi_12 = 28.0 entry_ewo_12 = 1.8 entry_cti_12 = -0.7 entry_ma_offset_13 = 0.99 entry_cti_13 = -0.82 entry_ewo_13 = -9.0 entry_ema_open_mult_14 = 0.014 entry_bb_offset_14 = 0.988 entry_ma_offset_14 = 0.945 entry_cti_14 = -0.86 entry_ema_open_mult_15 = 0.024 entry_ma_offset_15 = 0.958 entry_rsi_15 = 28.0 entry_ema_rel_15 = 0.974 entry_ma_offset_16 = 0.953 entry_rsi_16 = 31.0 entry_ewo_16 = 2.8 entry_cti_16 = -0.84 entry_ma_offset_17 = 0.99 entry_ewo_17 = -9.4 entry_cti_17 = -0.96 entry_volume_17 = 2.0 entry_rsi_18 = 33.0 entry_bb_offset_18 = 0.986 entry_volume_18 = 2.0 entry_cti_18 = -0.86 entry_rsi_1h_min_19 = 30.0 entry_chop_max_19 = 21.3 entry_rsi_20 = 36.0 entry_rsi_1h_20 = 16.0 entry_cti_20 = -0.84 entry_volume_20 = 2.0 entry_rsi_21 = 14.0 entry_rsi_1h_21 = 28.0 entry_cti_21 = -0.902 entry_volume_21 = 2.0 entry_volume_22 = 2.0 entry_bb_offset_22 = 0.984 entry_ma_offset_22 = 0.942 entry_ewo_22 = 5.8 entry_rsi_22 = 36.0 entry_bb_offset_23 = 0.985 entry_ewo_23 = 6.2 entry_rsi_23 = 32.4 entry_rsi_1h_23 = 70.0 entry_24_rsi_max = 50.0 entry_24_rsi_1h_min = 66.9 entry_25_ma_offset = 0.922 entry_25_rsi_4 = 38.0 entry_25_cti = -0.76 entry_26_zema_low_offset = 0.9 entry_26_cti = -0.9 entry_26_r = -80.0 entry_26_r_1h = -80.0 entry_26_volume = 2.0 entry_27_wr_max = 90.0 entry_27_wr_1h_max = 90.0 entry_27_rsi_max = 50 entry_27_cti = -0.93 entry_27_volume = 2.0 entry_28_ma_offset = 0.97 entry_28_ewo = 7.2 entry_28_rsi = 32.5 entry_28_cti = -0.9 entry_29_ma_offset = 0.94 entry_29_ewo = -4.0 entry_29_cti = -0.95 entry_30_ma_offset = 0.97 entry_30_ewo = 7.4 entry_30_rsi = 40.0 entry_30_cti = -0.88 entry_31_ma_offset = 0.94 entry_31_ewo = -19.0 entry_31_wr = -98.4 entry_32_ma_offset = 0.934 entry_32_dip = 0.005 entry_32_rsi = 46.0 entry_32_cti = -0.8 entry_33_ma_offset = 0.988 entry_33_rsi = 32.0 entry_33_cti = -0.88 entry_33_ewo = 6.4 entry_33_volume = 2.0 entry_34_ma_offset = 0.93 entry_34_dip = 0.005 entry_34_ewo = -6.0 entry_34_cti = -0.88 entry_34_volume = 2.0 entry_35_ma_offset = 0.984 entry_35_ewo = 9.6 entry_35_rsi = 32.0 entry_35_cti = -0.5 entry_36_ma_offset = 0.98 entry_36_ewo = -8.8 entry_36_cti = -0.8 entry_37_ma_offset = 0.98 entry_37_ewo = 9.8 entry_37_rsi = 56.0 entry_37_cti = -0.7 entry_38_ma_offset = 0.98 entry_38_ewo = -5.2 entry_38_cti = -0.96 entry_39_cti = -0.77 entry_39_r = -60.0 entry_39_r_1h = -38.0 entry_40_hrsi = 30.0 entry_40_cci = -240.0 entry_40_rsi = 30.0 entry_40_cti = -0.8 entry_40_r = -90.0 entry_40_r_1h = -90.0 entry_41_cti_1h = -0.84 entry_41_r_1h = -42.0 entry_41_ma_offset = 0.96 entry_41_cti = -0.8 entry_41_r = -75.0 entry_42_cti_1h = 0.5 entry_42_r_1h = -46.0 entry_42_ema_open_mult = 0.018 entry_42_bb_offset = 0.992 entry_43_cti_1h = 0.5 entry_43_r_1h = -80.0 entry_43_bb40_bbdelta_close = 0.046 entry_43_bb40_closedelta_close = 0.02 entry_43_bb40_tail_bbdelta = 0.5 entry_43_cti = -0.6 entry_43_r = -90.0 # Sell exit_condition_1_enable = True exit_condition_2_enable = True exit_condition_3_enable = True exit_condition_4_enable = True exit_condition_5_enable = True exit_condition_6_enable = True exit_condition_7_enable = True exit_condition_8_enable = True # 48h for pump exit checks exit_pump_threshold_48_1 = 0.9 exit_pump_threshold_48_2 = 0.7 exit_pump_threshold_48_3 = 0.5 # 36h for pump exit checks exit_pump_threshold_36_1 = 0.72 exit_pump_threshold_36_2 = 4.0 exit_pump_threshold_36_3 = 1.0 # 24h for pump exit checks exit_pump_threshold_24_1 = 0.68 exit_pump_threshold_24_2 = 0.62 exit_pump_threshold_24_3 = 0.88 exit_rsi_bb_1 = 79.5 exit_rsi_bb_2 = 81 exit_rsi_main_3 = 82 exit_dual_rsi_rsi_4 = 73.4 exit_dual_rsi_rsi_1h_4 = 79.6 exit_ema_relative_5 = 0.024 exit_rsi_diff_5 = 4.4 exit_rsi_under_6 = 79.0 exit_rsi_1h_7 = 81.7 exit_bb_relative_8 = 1.1 # Profit over EMA200 exit_custom_profit_bull_0 = 0.012 exit_custom_rsi_under_bull_0 = 34.0 exit_custom_profit_bull_1 = 0.02 exit_custom_rsi_under_bull_1 = 35.0 exit_custom_profit_bull_2 = 0.03 exit_custom_rsi_under_bull_2 = 36.0 exit_custom_profit_bull_3 = 0.04 exit_custom_rsi_under_bull_3 = 37.0 exit_custom_profit_bull_4 = 0.05 exit_custom_rsi_under_bull_4 = 42.0 exit_custom_profit_bull_5 = 0.06 exit_custom_rsi_under_bull_5 = 45.0 exit_custom_profit_bull_6 = 0.07 exit_custom_rsi_under_bull_6 = 48.0 exit_custom_profit_bull_7 = 0.08 exit_custom_rsi_under_bull_7 = 54.0 exit_custom_profit_bull_8 = 0.09 exit_custom_rsi_under_bull_8 = 50.0 exit_custom_profit_bull_9 = 0.1 exit_custom_rsi_under_bull_9 = 46.0 exit_custom_profit_bull_10 = 0.12 exit_custom_rsi_under_bull_10 = 42.0 exit_custom_profit_bull_11 = 0.2 exit_custom_rsi_under_bull_11 = 30.0 exit_custom_profit_bear_0 = 0.012 exit_custom_rsi_under_bear_0 = 34.0 exit_custom_profit_bear_1 = 0.02 exit_custom_rsi_under_bear_1 = 35.0 exit_custom_profit_bear_2 = 0.03 exit_custom_rsi_under_bear_2 = 37.0 exit_custom_profit_bear_3 = 0.04 exit_custom_rsi_under_bear_3 = 44.0 exit_custom_profit_bear_4 = 0.05 exit_custom_rsi_under_bear_4 = 48.0 exit_custom_profit_bear_5 = 0.06 exit_custom_rsi_under_bear_5 = 50.0 exit_custom_rsi_over_bear_5 = 78.0 exit_custom_profit_bear_6 = 0.07 exit_custom_rsi_under_bear_6 = 52.0 exit_custom_rsi_over_bear_6 = 78.0 exit_custom_profit_bear_7 = 0.08 exit_custom_rsi_under_bear_7 = 54.0 exit_custom_rsi_over_bear_7 = 80.0 exit_custom_profit_bear_8 = 0.09 exit_custom_rsi_under_bear_8 = 52.0 exit_custom_rsi_over_bear_8 = 82.0 exit_custom_profit_bear_9 = 0.1 exit_custom_rsi_under_bear_9 = 46.0 exit_custom_profit_bear_10 = 0.12 exit_custom_rsi_under_bear_10 = 42.0 exit_custom_profit_bear_11 = 0.2 exit_custom_rsi_under_bear_11 = 30.0 # Profit under EMA200 exit_custom_under_profit_bull_0 = 0.01 exit_custom_under_rsi_under_bull_0 = 38.0 exit_custom_under_profit_bull_1 = 0.02 exit_custom_under_rsi_under_bull_1 = 46.0 exit_custom_under_profit_bull_2 = 0.03 exit_custom_under_rsi_under_bull_2 = 47.0 exit_custom_under_profit_bull_3 = 0.04 exit_custom_under_rsi_under_bull_3 = 48.0 exit_custom_under_profit_bull_4 = 0.05 exit_custom_under_rsi_under_bull_4 = 49.0 exit_custom_under_profit_bull_5 = 0.06 exit_custom_under_rsi_under_bull_5 = 50.0 exit_custom_under_profit_bull_6 = 0.07 exit_custom_under_rsi_under_bull_6 = 52.0 exit_custom_under_profit_bull_7 = 0.08 exit_custom_under_rsi_under_bull_7 = 54.0 exit_custom_under_profit_bull_8 = 0.09 exit_custom_under_rsi_under_bull_8 = 50.0 exit_custom_under_profit_bull_9 = 0.1 exit_custom_under_rsi_under_bull_9 = 46.0 exit_custom_under_profit_bull_10 = 0.12 exit_custom_under_rsi_under_bull_10 = 42.0 exit_custom_under_profit_bull_11 = 0.2 exit_custom_under_rsi_under_bull_11 = 30.0 exit_custom_under_profit_bear_0 = 0.01 exit_custom_under_rsi_under_bear_0 = 38.0 exit_custom_under_profit_bear_1 = 0.02 exit_custom_under_rsi_under_bear_1 = 56.0 exit_custom_under_profit_bear_2 = 0.03 exit_custom_under_rsi_under_bear_2 = 57.0 exit_custom_under_profit_bear_3 = 0.04 exit_custom_under_rsi_under_bear_3 = 58.0 exit_custom_under_profit_bear_4 = 0.05 exit_custom_under_rsi_under_bear_4 = 57.0 exit_custom_under_profit_bear_5 = 0.06 exit_custom_under_rsi_under_bear_5 = 56.0 exit_custom_under_rsi_over_bear_5 = 78.0 exit_custom_under_profit_bear_6 = 0.07 exit_custom_under_rsi_under_bear_6 = 55.0 exit_custom_under_rsi_over_bear_6 = 78.0 exit_custom_under_profit_bear_7 = 0.08 exit_custom_under_rsi_under_bear_7 = 54.0 exit_custom_under_rsi_over_bear_7 = 80.0 exit_custom_under_profit_bear_8 = 0.09 exit_custom_under_rsi_under_bear_8 = 50.0 exit_custom_under_rsi_over_bear_8 = 82.0 exit_custom_under_profit_bear_9 = 0.1 exit_custom_under_rsi_under_bear_9 = 46.0 exit_custom_under_profit_bear_10 = 0.12 exit_custom_under_rsi_under_bear_10 = 42.0 exit_custom_under_profit_bear_11 = 0.2 exit_custom_under_rsi_under_bear_11 = 30.0 # Profit targets for pumped pairs 48h 1 exit_custom_pump_profit_1_1 = 0.01 exit_custom_pump_rsi_1_1 = 34.0 exit_custom_pump_profit_1_2 = 0.02 exit_custom_pump_rsi_1_2 = 40.0 exit_custom_pump_profit_1_3 = 0.04 exit_custom_pump_rsi_1_3 = 42.0 exit_custom_pump_profit_1_4 = 0.1 exit_custom_pump_rsi_1_4 = 34.0 exit_custom_pump_profit_1_5 = 0.2 exit_custom_pump_rsi_1_5 = 30.0 # Profit targets for pumped pairs 36h 1 exit_custom_pump_profit_2_1 = 0.01 exit_custom_pump_rsi_2_1 = 34.0 exit_custom_pump_profit_2_2 = 0.02 exit_custom_pump_rsi_2_2 = 40.0 exit_custom_pump_profit_2_3 = 0.04 exit_custom_pump_rsi_2_3 = 42.0 exit_custom_pump_profit_2_4 = 0.1 exit_custom_pump_rsi_2_4 = 34.0 exit_custom_pump_profit_2_5 = 0.2 exit_custom_pump_rsi_2_5 = 30.0 # Profit targets for pumped pairs 24h 1 exit_custom_pump_profit_3_1 = 0.01 exit_custom_pump_rsi_3_1 = 34.0 exit_custom_pump_profit_3_2 = 0.02 exit_custom_pump_rsi_3_2 = 40.0 exit_custom_pump_profit_3_3 = 0.04 exit_custom_pump_rsi_3_3 = 42.0 exit_custom_pump_profit_3_4 = 0.1 exit_custom_pump_rsi_3_4 = 34.0 exit_custom_pump_profit_3_5 = 0.2 exit_custom_pump_rsi_3_5 = 30.0 # SMA descending exit_custom_dec_profit_min_1 = 0.05 exit_custom_dec_profit_max_1 = 0.12 # Under EMA100 exit_custom_dec_profit_min_2 = 0.07 exit_custom_dec_profit_max_2 = 0.16 # Trail 1 exit_trail_profit_min_1 = 0.03 exit_trail_profit_max_1 = 0.05 exit_trail_down_1 = 0.05 exit_trail_rsi_min_1 = 10.0 exit_trail_rsi_max_1 = 20.0 # Trail 2 exit_trail_profit_min_2 = 0.1 exit_trail_profit_max_2 = 0.4 exit_trail_down_2 = 0.03 exit_trail_rsi_min_2 = 20.0 exit_trail_rsi_max_2 = 50.0 # Trail 3 exit_trail_profit_min_3 = 0.06 exit_trail_profit_max_3 = 0.2 exit_trail_down_3 = 0.05 # Trail 4 exit_trail_profit_min_4 = 0.03 exit_trail_profit_max_4 = 0.06 exit_trail_down_4 = 0.02 # Under & near EMA200, accept profit exit_custom_profit_under_profit_min_1 = 0.001 exit_custom_profit_under_profit_max_1 = 0.01 exit_custom_profit_under_rel_1 = 0.024 exit_custom_profit_under_rsi_diff_1 = 4.4 exit_custom_profit_under_profit_2 = 0.03 exit_custom_profit_under_rel_2 = 0.024 exit_custom_profit_under_rsi_diff_2 = 4.4 # Under & near EMA200, take the loss exit_custom_stoploss_under_rel_1 = 0.002 exit_custom_stoploss_under_rsi_diff_1 = 10.0 # Long duration/recover stoploss 1 exit_custom_stoploss_long_profit_min_1 = -0.08 exit_custom_stoploss_long_profit_max_1 = -0.04 exit_custom_stoploss_long_recover_1 = 0.14 exit_custom_stoploss_long_rsi_diff_1 = 4.0 # Long duration/recover stoploss 2 exit_custom_stoploss_long_recover_2 = 0.06 exit_custom_stoploss_long_rsi_diff_2 = 40.0 # Pumped, descending SMA exit_custom_pump_dec_profit_min_1 = 0.005 exit_custom_pump_dec_profit_max_1 = 0.05 exit_custom_pump_dec_profit_min_2 = 0.04 exit_custom_pump_dec_profit_max_2 = 0.06 exit_custom_pump_dec_profit_min_3 = 0.06 exit_custom_pump_dec_profit_max_3 = 0.09 exit_custom_pump_dec_profit_min_4 = 0.02 exit_custom_pump_dec_profit_max_4 = 0.04 # Pumped 48h 1, under EMA200 exit_custom_pump_under_profit_min_1 = 0.04 exit_custom_pump_under_profit_max_1 = 0.09 # Pumped trail 1 exit_custom_pump_trail_profit_min_1 = 0.05 exit_custom_pump_trail_profit_max_1 = 0.07 exit_custom_pump_trail_down_1 = 0.05 exit_custom_pump_trail_rsi_min_1 = 20.0 exit_custom_pump_trail_rsi_max_1 = 70.0 # Stoploss, pumped, 48h 1 exit_custom_stoploss_pump_max_profit_1 = 0.01 exit_custom_stoploss_pump_min_1 = -0.02 exit_custom_stoploss_pump_max_1 = -0.01 exit_custom_stoploss_pump_ma_offset_1 = 0.94 # Stoploss, pumped, 48h 1 exit_custom_stoploss_pump_max_profit_2 = 0.025 exit_custom_stoploss_pump_loss_2 = -0.05 exit_custom_stoploss_pump_ma_offset_2 = 0.92 # Stoploss, pumped, 36h 3 exit_custom_stoploss_pump_max_profit_3 = 0.008 exit_custom_stoploss_pump_loss_3 = -0.12 exit_custom_stoploss_pump_ma_offset_3 = 0.88 # Recover exit_custom_recover_profit_1 = 0.06 exit_custom_recover_min_loss_1 = 0.12 exit_custom_recover_profit_min_2 = 0.01 exit_custom_recover_profit_max_2 = 0.05 exit_custom_recover_min_loss_2 = 0.06 exit_custom_recover_rsi_2 = 46.0 # Profit for long duration trades exit_custom_long_profit_min_1 = 0.03 exit_custom_long_profit_max_1 = 0.04 exit_custom_long_duration_min_1 = 900 ############################################################# hold_trades_cache = None # Profit Target Signal profit_target_1_enable = True @staticmethod def get_hold_trades_config_file(): strat_file_path = pathlib.Path(__file__) hold_trades_config_file_resolve = strat_file_path.resolve().parent / 'hold-trades.json' if hold_trades_config_file_resolve.is_file(): return hold_trades_config_file_resolve # The resolved path does not exist, is it a symlink? hold_trades_config_file_absolute = strat_file_path.absolute().parent / 'hold-trades.json' if hold_trades_config_file_absolute.is_file(): return hold_trades_config_file_absolute if hold_trades_config_file_resolve != hold_trades_config_file_absolute: looked_in = f"'{hold_trades_config_file_resolve}' and '{hold_trades_config_file_absolute}'" else: looked_in = f"'{hold_trades_config_file_resolve}'" log.warning("The 'hold-trades.json' file was not found. Looked in %s. HOLD support disabled.", looked_in) def load_hold_trades_config(self): if self.hold_trades_cache is None: hold_trades_config_file = Github_DerSalvador_freqtrade_helm_chart__NostalgiaForInfinityNext_maximizer__20260416_224245.get_hold_trades_config_file() if hold_trades_config_file: self.hold_trades_cache = HoldsCache(hold_trades_config_file) if self.hold_trades_cache: self.hold_trades_cache.load() def bot_loop_start(self, **kwargs) -> None: """ Called at the start of the bot iteration (one loop). Might be used to perform pair-independent tasks (e.g. gather some remote resource for comparison) :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. """ if self.config['runmode'].value in ('live', 'dry_run'): self.load_hold_trades_config() # Load custom_info for initial else save every loop start if not self.custom_info: self.custom_info = get_profit_target_by_pair() else: save_profit_target_by_pair(self.custom_info) def get_ticker_indicator(self): return int(self.timeframe[:-1]) def exit_over_main(self, current_profit: float, last_candle) -> tuple: if last_candle['close'] > last_candle['ema_200']: if last_candle['moderi_96']: if current_profit >= self.exit_custom_profit_bull_11: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_11: return (True, 'signal_profit_o_bull_11') elif self.exit_custom_profit_bull_11 > current_profit >= self.exit_custom_profit_bull_10: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_10: return (True, 'signal_profit_o_bull_10') elif self.exit_custom_profit_bull_10 > current_profit >= self.exit_custom_profit_bull_9: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_9: return (True, 'signal_profit_o_bull_9') elif self.exit_custom_profit_bull_9 > current_profit >= self.exit_custom_profit_bull_8: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_8: return (True, 'signal_profit_o_bull_8') elif self.exit_custom_profit_bull_8 > current_profit >= self.exit_custom_profit_bull_7: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_7: return (True, 'signal_profit_o_bull_7') elif self.exit_custom_profit_bull_7 > current_profit >= self.exit_custom_profit_bull_6: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_6 and last_candle['cmf'] < 0.0: return (True, 'signal_profit_o_bull_6') elif self.exit_custom_profit_bull_6 > current_profit >= self.exit_custom_profit_bull_5: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_5 and last_candle['cmf'] < 0.0: return (True, 'signal_profit_o_bull_5') elif self.exit_custom_profit_bull_5 > current_profit >= self.exit_custom_profit_bull_4: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_4 and last_candle['cmf'] < 0.0: return (True, 'signal_profit_o_bull_4') elif self.exit_custom_profit_bull_4 > current_profit >= self.exit_custom_profit_bull_3: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_3 and last_candle['cmf'] < 0.0: return (True, 'signal_profit_o_bull_3') elif self.exit_custom_profit_bull_3 > current_profit >= self.exit_custom_profit_bull_2: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_2 and last_candle['cmf'] < 0.0: return (True, 'signal_profit_o_bull_2') elif self.exit_custom_profit_bull_2 > current_profit >= self.exit_custom_profit_bull_1: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_1 and last_candle['cmf'] < 0.0: return (True, 'signal_profit_o_bull_1') elif self.exit_custom_profit_bull_1 > current_profit >= self.exit_custom_profit_bull_0: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_0 and last_candle['cmf'] < 0.0: return (True, 'signal_profit_o_bull_0') elif current_profit >= self.exit_custom_profit_bear_11: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_11: return (True, 'signal_profit_o_bear_11') elif self.exit_custom_profit_bear_11 > current_profit >= self.exit_custom_profit_bear_10: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_10: return (True, 'signal_profit_o_bear_10') elif self.exit_custom_profit_bear_10 > current_profit >= self.exit_custom_profit_bear_9: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_9: return (True, 'signal_profit_o_bear_9') elif self.exit_custom_profit_bear_9 > current_profit >= self.exit_custom_profit_bear_8: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_8: return (True, 'signal_profit_o_bear_8_1') elif last_candle['rsi_14'] > self.exit_custom_rsi_over_bear_8: return (True, 'signal_profit_o_bear_8_2') elif self.exit_custom_profit_bear_8 > current_profit >= self.exit_custom_profit_bear_7: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_7: return (True, 'signal_profit_o_bear_7_1') elif last_candle['rsi_14'] > self.exit_custom_rsi_over_bear_7: return (True, 'signal_profit_o_bear_7_2') elif self.exit_custom_profit_bear_7 > current_profit >= self.exit_custom_profit_bear_6: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_6: return (True, 'signal_profit_o_bear_6_1') elif last_candle['rsi_14'] > self.exit_custom_rsi_over_bear_6: return (True, 'signal_profit_o_bear_6_2') elif self.exit_custom_profit_bear_6 > current_profit >= self.exit_custom_profit_bear_5: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_5: return (True, 'signal_profit_o_bear_5_1') elif last_candle['rsi_14'] > self.exit_custom_rsi_over_bear_5: return (True, 'signal_profit_o_bear_5_2') elif self.exit_custom_profit_bear_5 > current_profit >= self.exit_custom_profit_bear_4: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_4: return (True, 'signal_profit_o_bear_4') elif self.exit_custom_profit_bear_4 > current_profit >= self.exit_custom_profit_bear_3: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_3 and last_candle['cmf'] < 0.0: return (True, 'signal_profit_o_bear_3') elif self.exit_custom_profit_bear_3 > current_profit >= self.exit_custom_profit_bear_2: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_2 and last_candle['cmf'] < 0.0: return (True, 'signal_profit_o_bear_2') elif self.exit_custom_profit_bear_2 > current_profit >= self.exit_custom_profit_bear_1: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_1 and last_candle['cmf'] < 0.0: return (True, 'signal_profit_o_bear_1') elif self.exit_custom_profit_bear_1 > current_profit >= self.exit_custom_profit_bear_0: if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_0 and last_candle['cmf'] < 0.0: return (True, 'signal_profit_o_bear_0') return (False, None) def exit_under_main(self, current_profit: float, last_candle) -> tuple: if last_candle['close'] < last_candle['ema_200']: if last_candle['moderi_96']: if current_profit >= self.exit_custom_under_profit_bull_11: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_11: return (True, 'signal_profit_u_bull_11') elif self.exit_custom_under_profit_bull_11 > current_profit >= self.exit_custom_under_profit_bull_10: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_10: return (True, 'signal_profit_u_bull_10') elif self.exit_custom_under_profit_bull_10 > current_profit >= self.exit_custom_under_profit_bull_9: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_9: return (True, 'signal_profit_u_bull_9') elif self.exit_custom_under_profit_bull_9 > current_profit >= self.exit_custom_under_profit_bull_8: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_8: return (True, 'signal_profit_u_bull_8') elif self.exit_custom_under_profit_bull_8 > current_profit >= self.exit_custom_under_profit_bull_7: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_7: return (True, 'signal_profit_u_bull_7') elif self.exit_custom_under_profit_bull_7 > current_profit >= self.exit_custom_under_profit_bull_6: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_6: return (True, 'signal_profit_u_bull_6') elif self.exit_custom_under_profit_bull_6 > current_profit >= self.exit_custom_under_profit_bull_5: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_5: return (True, 'signal_profit_u_bull_5') elif self.exit_custom_under_profit_bull_5 > current_profit >= self.exit_custom_under_profit_bull_4: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_4: return (True, 'signal_profit_u_bull_4') elif self.exit_custom_under_profit_bull_4 > current_profit >= self.exit_custom_under_profit_bull_3: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_3: return (True, 'signal_profit_u_bull_3') elif self.exit_custom_under_profit_bull_3 > current_profit >= self.exit_custom_under_profit_bull_2: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_2: return (True, 'signal_profit_u_bull_2') elif self.exit_custom_under_profit_bull_2 > current_profit >= self.exit_custom_under_profit_bull_1: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_1: return (True, 'signal_profit_u_bull_1') elif self.exit_custom_under_profit_bull_1 > current_profit >= self.exit_custom_under_profit_bull_0: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_0 and last_candle['cmf'] < 0.0: return (True, 'signal_profit_u_bull_0') elif current_profit >= self.exit_custom_under_profit_bear_11: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_11: return (True, 'signal_profit_u_bear_11') elif self.exit_custom_under_profit_bear_11 > current_profit >= self.exit_custom_under_profit_bear_10: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_10: return (True, 'signal_profit_u_bear_10') elif self.exit_custom_under_profit_bear_10 > current_profit >= self.exit_custom_under_profit_bear_9: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_9: return (True, 'signal_profit_u_bear_9') elif self.exit_custom_under_profit_bear_9 > current_profit >= self.exit_custom_under_profit_bear_8: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_8: return (True, 'signal_profit_u_bear_8_1') elif last_candle['rsi_14'] > self.exit_custom_under_rsi_over_bear_8: return (True, 'signal_profit_u_bear_8_2') elif self.exit_custom_under_profit_bear_8 > current_profit >= self.exit_custom_under_profit_bear_7: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_7: return (True, 'signal_profit_u_bear_7_1') elif last_candle['rsi_14'] > self.exit_custom_under_rsi_over_bear_7: return (True, 'signal_profit_u_bear_7_2') elif self.exit_custom_under_profit_bear_7 > current_profit >= self.exit_custom_under_profit_bear_6: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_6: return (True, 'signal_profit_u_bear_6_1') elif last_candle['rsi_14'] > self.exit_custom_under_rsi_over_bear_6: return (True, 'signal_profit_u_bear_6_2') elif self.exit_custom_under_profit_bear_6 > current_profit >= self.exit_custom_under_profit_bear_5: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_5: return (True, 'signal_profit_u_bear_5_1') elif last_candle['rsi_14'] > self.exit_custom_under_rsi_over_bear_5: return (True, 'signal_profit_u_bear_5_2') elif self.exit_custom_under_profit_bear_5 > current_profit >= self.exit_custom_under_profit_bear_4: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_4: return (True, 'signal_profit_u_bear_4') elif self.exit_custom_under_profit_bear_4 > current_profit >= self.exit_custom_under_profit_bear_3: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_3: return (True, 'signal_profit_u_bear_3') elif self.exit_custom_under_profit_bear_3 > current_profit >= self.exit_custom_under_profit_bear_2: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_2: return (True, 'signal_profit_u_bear_2') elif self.exit_custom_under_profit_bear_2 > current_profit >= self.exit_custom_under_profit_bear_1: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_1: return (True, 'signal_profit_u_bear_1') elif self.exit_custom_under_profit_bear_1 > current_profit >= self.exit_custom_under_profit_bear_0: if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_0 and last_candle['cmf'] < 0.0: return (True, 'signal_profit_u_bear_0') return (False, None) def exit_pump_main(self, current_profit: float, last_candle) -> tuple: if last_candle['exit_pump_48_1_1h']: if current_profit >= self.exit_custom_pump_profit_1_5: if last_candle['rsi_14'] < self.exit_custom_pump_rsi_1_5: return (True, 'signal_profit_p_1_5') elif self.exit_custom_pump_profit_1_5 > current_profit >= self.exit_custom_pump_profit_1_4: if last_candle['rsi_14'] < self.exit_custom_pump_rsi_1_4: return (True, 'signal_profit_p_1_4') elif self.exit_custom_pump_profit_1_4 > current_profit >= self.exit_custom_pump_profit_1_3: if last_candle['rsi_14'] < self.exit_custom_pump_rsi_1_3: return (True, 'signal_profit_p_1_3') elif self.exit_custom_pump_profit_1_3 > current_profit >= self.exit_custom_pump_profit_1_2: if last_candle['rsi_14'] < self.exit_custom_pump_rsi_1_2: return (True, 'signal_profit_p_1_2') elif self.exit_custom_pump_profit_1_2 > current_profit >= self.exit_custom_pump_profit_1_1: if last_candle['rsi_14'] < self.exit_custom_pump_rsi_1_1: return (True, 'signal_profit_p_1_1') elif last_candle['exit_pump_36_1_1h']: if current_profit >= self.exit_custom_pump_profit_2_5: if last_candle['rsi_14'] < self.exit_custom_pump_rsi_2_5: return (True, 'signal_profit_p_2_5') elif self.exit_custom_pump_profit_2_5 > current_profit >= self.exit_custom_pump_profit_2_4: if last_candle['rsi_14'] < self.exit_custom_pump_rsi_2_4: return (True, 'signal_profit_p_2_4') elif self.exit_custom_pump_profit_2_4 > current_profit >= self.exit_custom_pump_profit_2_3: if last_candle['rsi_14'] < self.exit_custom_pump_rsi_2_3: return (True, 'signal_profit_p_2_3') elif self.exit_custom_pump_profit_2_3 > current_profit >= self.exit_custom_pump_profit_2_2: if last_candle['rsi_14'] < self.exit_custom_pump_rsi_2_2: return (True, 'signal_profit_p_2_2') elif self.exit_custom_pump_profit_2_2 > current_profit >= self.exit_custom_pump_profit_2_1: if last_candle['rsi_14'] < self.exit_custom_pump_rsi_2_1: return (True, 'signal_profit_p_2_1') elif last_candle['exit_pump_24_1_1h']: if current_profit >= self.exit_custom_pump_profit_3_5: if last_candle['rsi_14'] < self.exit_custom_pump_rsi_3_5: return (True, 'signal_profit_p_3_5') elif self.exit_custom_pump_profit_3_5 > current_profit >= self.exit_custom_pump_profit_3_4: if last_candle['rsi_14'] < self.exit_custom_pump_rsi_3_4: return (True, 'signal_profit_p_3_4') elif self.exit_custom_pump_profit_3_4 > current_profit >= self.exit_custom_pump_profit_3_3: if last_candle['rsi_14'] < self.exit_custom_pump_rsi_3_3: return (True, 'signal_profit_p_3_3') elif self.exit_custom_pump_profit_3_3 > current_profit >= self.exit_custom_pump_profit_3_2: if last_candle['rsi_14'] < self.exit_custom_pump_rsi_3_2: return (True, 'signal_profit_p_3_2') elif self.exit_custom_pump_profit_3_2 > current_profit >= self.exit_custom_pump_profit_3_1: if last_candle['rsi_14'] < self.exit_custom_pump_rsi_3_1: return (True, 'signal_profit_p_3_1') return (False, None) def exit_dec_main(self, current_profit: float, last_candle) -> tuple: if self.exit_custom_dec_profit_max_1 > current_profit >= self.exit_custom_dec_profit_min_1 and last_candle['sma_200_dec_20']: return (True, 'signal_profit_d_1') elif self.exit_custom_dec_profit_max_2 > current_profit >= self.exit_custom_dec_profit_min_2 and last_candle['close'] < last_candle['ema_100']: return (True, 'signal_profit_d_2') return (False, None) def exit_trail_main(self, current_profit: float, last_candle, max_profit: float) -> tuple: if self.exit_trail_profit_max_1 > current_profit >= self.exit_trail_profit_min_1 and self.exit_trail_rsi_min_1 < last_candle['rsi_14'] < self.exit_trail_rsi_max_1 and (max_profit > current_profit + self.exit_trail_down_1) and (last_candle['moderi_96'] == False): return (True, 'signal_profit_t_1') elif self.exit_trail_profit_max_2 > current_profit >= self.exit_trail_profit_min_2 and self.exit_trail_rsi_min_2 < last_candle['rsi_14'] < self.exit_trail_rsi_max_2 and (max_profit > current_profit + self.exit_trail_down_2) and (last_candle['ema_25'] < last_candle['ema_50']): return (True, 'signal_profit_t_2') elif self.exit_trail_profit_max_3 > current_profit >= self.exit_trail_profit_min_3 and max_profit > current_profit + self.exit_trail_down_3 and last_candle['sma_200_dec_20_1h']: return (True, 'signal_profit_t_3') elif self.exit_trail_profit_max_4 > current_profit >= self.exit_trail_profit_min_4 and max_profit > current_profit + self.exit_trail_down_4 and last_candle['sma_200_dec_24'] and (last_candle['cmf'] < 0.0): return (True, 'signal_profit_t_4') return (False, None) def exit_duration_main(self, current_profit: float, last_candle, trade: 'Trade', current_time: 'datetime') -> tuple: # Pumped pair, short duration if last_candle['exit_pump_24_1_1h'] and 0.2 > current_profit >= 0.07 and (current_time - timedelta(minutes=30) < trade.open_date_utc): return (True, 'signal_profit_p_s_1') elif self.exit_custom_long_profit_min_1 < current_profit < self.exit_custom_long_profit_max_1 and current_time - timedelta(minutes=self.exit_custom_long_duration_min_1) > trade.open_date_utc: return (True, 'signal_profit_l_1') return (False, None) def exit_under_min(self, current_profit: float, last_candle) -> tuple: # if ((last_candle['moderi_96']) == False): # Downtrend # if (self.exit_custom_profit_under_profit_max_1 > current_profit >= self.exit_custom_profit_under_profit_min_1) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.exit_custom_profit_under_rel_1) and (last_candle['rsi_14'] > last_candle['rsi_14_1h'] + self.exit_custom_profit_under_rsi_diff_1): # return True, 'signal_profit_u_e_1' # else: # Uptrend if current_profit >= self.exit_custom_profit_under_profit_2 and last_candle['close'] < last_candle['ema_200'] and ((last_candle['ema_200'] - last_candle['close']) / last_candle['close'] < self.exit_custom_profit_under_rel_2) and (last_candle['rsi_14'] > last_candle['rsi_14_1h'] + self.exit_custom_profit_under_rsi_diff_2): return (True, 'signal_profit_u_e_2') return (False, None) def exit_stoploss(self, current_profit: float, last_candle, previous_candle_1) -> tuple: if -0.12 <= current_profit < -0.08: if last_candle['close'] < last_candle['atr_high_thresh_1'] and previous_candle_1['close'] > previous_candle_1['atr_high_thresh_1']: return (True, 'signal_stoploss_atr_1') elif -0.16 <= current_profit < -0.12: if last_candle['close'] < last_candle['atr_high_thresh_2'] and previous_candle_1['close'] > previous_candle_1['atr_high_thresh_2']: return (True, 'signal_stoploss_atr_2') elif -0.2 <= current_profit < -0.16: if last_candle['close'] < last_candle['atr_high_thresh_3'] and previous_candle_1['close'] > previous_candle_1['atr_high_thresh_3']: return (True, 'signal_stoploss_atr_3') elif current_profit < -0.2: if last_candle['close'] < last_candle['atr_high_thresh_4'] and previous_candle_1['close'] > previous_candle_1['atr_high_thresh_4']: return (True, 'signal_stoploss_atr_4') return (False, None) def exit_pump_dec(self, current_profit: float, last_candle) -> tuple: if self.exit_custom_pump_dec_profit_max_1 > current_profit >= self.exit_custom_pump_dec_profit_min_1 and last_candle['exit_pump_48_1_1h'] and last_candle['sma_200_dec_20'] and (last_candle['close'] < last_candle['ema_200']): return (True, 'signal_profit_p_d_1') elif self.exit_custom_pump_dec_profit_max_2 > current_profit >= self.exit_custom_pump_dec_profit_min_2 and last_candle['exit_pump_48_2_1h'] and last_candle['sma_200_dec_20'] and (last_candle['close'] < last_candle['ema_200']): return (True, 'signal_profit_p_d_2') elif self.exit_custom_pump_dec_profit_max_3 > current_profit >= self.exit_custom_pump_dec_profit_min_3 and last_candle['exit_pump_48_3_1h'] and last_candle['sma_200_dec_20'] and (last_candle['close'] < last_candle['ema_200']): return (True, 'signal_profit_p_d_3') elif self.exit_custom_pump_dec_profit_max_4 > current_profit >= self.exit_custom_pump_dec_profit_min_4 and last_candle['sma_200_dec_20'] and last_candle['exit_pump_24_2_1h']: return (True, 'signal_profit_p_d_4') return (False, None) def exit_pump_extra(self, current_profit: float, last_candle, max_profit: float) -> tuple: # Pumped 48h 1, under EMA200 if self.exit_custom_pump_under_profit_max_1 > current_profit >= self.exit_custom_pump_under_profit_min_1 and last_candle['exit_pump_48_1_1h'] and (last_candle['close'] < last_candle['ema_200']): return (True, 'signal_profit_p_u_1') # Pumped 36h 2, trail 1 elif last_candle['exit_pump_36_2_1h'] and self.exit_custom_pump_trail_profit_max_1 > current_profit >= self.exit_custom_pump_trail_profit_min_1 and (self.exit_custom_pump_trail_rsi_min_1 < last_candle['rsi_14'] < self.exit_custom_pump_trail_rsi_max_1) and (max_profit > current_profit + self.exit_custom_pump_trail_down_1): return (True, 'signal_profit_p_t_1') return (False, None) def exit_recover(self, current_profit: float, last_candle, max_loss: float) -> tuple: if max_loss > self.exit_custom_recover_min_loss_1 and current_profit >= self.exit_custom_recover_profit_1: return (True, 'signal_profit_r_1') elif max_loss > self.exit_custom_recover_min_loss_2 and self.exit_custom_recover_profit_max_2 > current_profit >= self.exit_custom_recover_profit_min_2 and (last_candle['rsi_14'] < self.exit_custom_recover_rsi_2) and (last_candle['ema_25'] < last_candle['ema_50']): return (True, 'signal_profit_r_2') return (False, None) def exit_r_1(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if last_candle['r_480'] > -0.5: return (True, 'signal_profit_w_1_1') elif 0.03 > current_profit >= 0.02: if last_candle['r_480'] > -0.6: return (True, 'signal_profit_w_1_2') elif 0.04 > current_profit >= 0.03: if last_candle['r_480'] > -0.7: return (True, 'signal_profit_w_1_3') elif 0.05 > current_profit >= 0.04: if last_candle['r_480'] > -0.8: return (True, 'signal_profit_w_1_4') elif 0.06 > current_profit >= 0.05: if last_candle['r_480'] > -0.9: return (True, 'signal_profit_w_1_5') elif 0.07 > current_profit >= 0.06: if last_candle['r_480'] > -2.0: return (True, 'signal_profit_w_1_6') elif 0.08 > current_profit >= 0.07: if last_candle['r_480'] > -2.2: return (True, 'signal_profit_w_1_7') elif 0.09 > current_profit >= 0.08: if last_candle['r_480'] > -2.4: return (True, 'signal_profit_w_1_8') elif 0.1 > current_profit >= 0.09: if last_candle['r_480'] > -2.6: return (True, 'signal_profit_w_1_9') elif 0.12 > current_profit >= 0.1: if last_candle['r_480'] > -2.5 and last_candle['rsi_14'] > 72.0: return (True, 'signal_profit_w_1_10') elif 0.2 > current_profit >= 0.12: if last_candle['r_480'] > -2.0 and last_candle['rsi_14'] > 78.0: return (True, 'signal_profit_w_1_11') elif current_profit >= 0.2: if last_candle['r_480'] > -1.0 and last_candle['rsi_14'] > 80.0: return (True, 'signal_profit_w_1_12') return (False, None) def exit_r_2(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if last_candle['r_480'] > -4.0 and last_candle['rsi_14'] > 79.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_2_1') elif 0.03 > current_profit >= 0.02: if last_candle['r_480'] > -4.1 and last_candle['rsi_14'] > 79.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_2_2') elif 0.04 > current_profit >= 0.03: if last_candle['r_480'] > -4.2 and last_candle['rsi_14'] > 79.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_2_3') elif 0.05 > current_profit >= 0.04: if last_candle['r_480'] > -4.3 and last_candle['rsi_14'] > 79.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_2_4') elif 0.06 > current_profit >= 0.05: if last_candle['r_480'] > -4.4 and last_candle['rsi_14'] > 79.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_2_5') elif 0.07 > current_profit >= 0.06: if last_candle['r_480'] > -4.5 and last_candle['rsi_14'] > 79.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_2_6') elif 0.08 > current_profit >= 0.07: if last_candle['r_480'] > -5.0 and last_candle['rsi_14'] > 80.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_2_7') elif 0.09 > current_profit >= 0.08: if last_candle['r_480'] > -5.0 and last_candle['rsi_14'] > 80.5 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_2_8') elif 0.1 > current_profit >= 0.09: if last_candle['r_480'] > -4.8 and last_candle['rsi_14'] > 80.5 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_2_9') elif 0.12 > current_profit >= 0.1: if last_candle['r_480'] > -4.4 and last_candle['rsi_14'] > 80.5 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_2_10') elif 0.2 > current_profit >= 0.12: if last_candle['r_480'] > -3.2 and last_candle['rsi_14'] > 81.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_2_11') elif current_profit >= 0.2: if last_candle['r_480'] > -3.0 and last_candle['rsi_14'] > 81.5 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_2_12') return (False, None) def exit_r_3(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if last_candle['r_480'] > -3.0 and last_candle['rsi_14'] > 74.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_3_1') elif 0.03 > current_profit >= 0.02: if last_candle['r_480'] > -3.5 and last_candle['rsi_14'] > 74.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_3_2') elif 0.04 > current_profit >= 0.03: if last_candle['r_480'] > -4.0 and last_candle['rsi_14'] > 74.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_3_3') elif 0.05 > current_profit >= 0.04: if last_candle['r_480'] > -4.5 and last_candle['rsi_14'] > 79.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return (True, 'signal_profit_w_3_4') return (False, None) def exit_r_4(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if last_candle['r_480'] > -2.0 and last_candle['rsi_14'] > 68.0 and (last_candle['cti'] > 0.9): return (True, 'signal_profit_w_4_1') elif 0.03 > current_profit >= 0.02: if last_candle['r_480'] > -2.5 and last_candle['rsi_14'] > 68.0 and (last_candle['cti'] > 0.9): return (True, 'signal_profit_w_4_2') elif 0.04 > current_profit >= 0.03: if last_candle['r_480'] > -3.0 and last_candle['rsi_14'] > 68.0 and (last_candle['cti'] > 0.9): return (True, 'signal_profit_w_4_3') elif 0.05 > current_profit >= 0.04: if last_candle['r_480'] > -3.5 and last_candle['rsi_14'] > 68.0 and (last_candle['cti'] > 0.9): return (True, 'signal_profit_w_4_4') elif 0.06 > current_profit >= 0.05: if last_candle['r_480'] > -4.0 and last_candle['rsi_14'] > 68.0 and (last_candle['cti'] > 0.9): return (True, 'signal_profit_w_4_5') elif 0.07 > current_profit >= 0.06: if last_candle['r_480'] > -4.5 and last_candle['rsi_14'] > 79.0 and (last_candle['cti'] > 0.9): return (True, 'signal_profit_w_4_6') elif 0.08 > current_profit >= 0.07: if last_candle['r_480'] > -5.0 and last_candle['rsi_14'] > 79.0 and (last_candle['cti'] > 0.9): return (True, 'signal_profit_w_4_7') elif 0.09 > current_profit >= 0.08: if last_candle['r_480'] > -5.5 and last_candle['rsi_14'] > 79.0 and (last_candle['cti'] > 0.9): return (True, 'signal_profit_w_4_8') elif 0.1 > current_profit >= 0.09: if last_candle['r_480'] > -4.0 and last_candle['rsi_14'] > 79.0 and (last_candle['cti'] > 0.9): return (True, 'signal_profit_w_4_9') elif 0.12 > current_profit >= 0.1: if last_candle['r_480'] > -3.0 and last_candle['rsi_14'] > 79.0 and (last_candle['cti'] > 0.9): return (True, 'signal_profit_w_4_10') elif 0.2 > current_profit >= 0.12: if last_candle['r_480'] > -2.5 and last_candle['rsi_14'] > 80.0 and (last_candle['cti'] > 0.9): return (True, 'signal_profit_w_4_11') elif current_profit >= 0.2: if last_candle['r_480'] > -2.0 and last_candle['rsi_14'] > 80.0 and (last_candle['cti'] > 0.9): return (True, 'signal_profit_w_4_12') return (False, None) def mark_profit_target(self, pair: str, trade: 'Trade', current_rate: float, current_profit: float, max_profit: float, last_candle, previous_candle_1) -> tuple: if self.profit_target_1_enable: if last_candle['moderi_96'] == False and self.exit_custom_profit_under_profit_max_1 > current_profit >= self.exit_custom_profit_under_profit_min_1 and (last_candle['close'] < last_candle['ema_200']) and ((last_candle['ema_200'] - last_candle['close']) / last_candle['close'] < self.exit_custom_profit_under_rel_1) and (last_candle['rsi_14'] > last_candle['rsi_14_1h'] + self.exit_custom_profit_under_rsi_diff_1): self._set_profit_target(pair, current_rate, 'mark_profit_target_01') def exit_profit_target(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, max_profit: float, last_candle, previous_candle_1) -> tuple: # Check if pair exist on custom_info if pair not in self.custom_info.keys(): return (False, None) previous_rate = self.custom_info[pair]['rate'] if self.profit_target_1_enable: if current_rate < previous_rate - 0.005: return (True, 'maximizer_stoploss') if last_candle['zlema_1_exit'] < last_candle['zlema_4_exit'] and previous_candle_1['zlema_1_exit'] > previous_candle_1['zlema_4_exit'] and (last_candle['hrsi_exit'] > 90): return (True, 'maximizer_strike') if current_rate < max_profit - 0.01: return (True, 'maximizer_trail') return (False, None) def exit_quick_mode(self, current_profit: float, max_profit: float, last_candle, previous_candle_1) -> tuple: if 0.06 > current_profit > 0.02 and last_candle['rsi_14'] > 80.0: return (True, 'signal_profit_q_1') if 0.06 > current_profit > 0.02 and last_candle['cti'] > 0.95: return (True, 'signal_profit_q_2') if last_candle['close'] < last_candle['atr_high_thresh_q'] and previous_candle_1['close'] > previous_candle_1['atr_high_thresh_q']: if 0.05 > current_profit > 0.02: return (True, 'signal_profit_q_atr') elif current_profit < -0.08: return (True, 'signal_stoploss_q_atr') if current_profit > 0.02 and last_candle['pm'] <= last_candle['pmax_thresh'] and (last_candle['close'] > last_candle['sma_21'] * 1.1): return (True, 'signal_profit_q_pmax_bull') if current_profit > 0.001 and last_candle['pm'] > last_candle['pmax_thresh'] and (last_candle['close'] > last_candle['sma_21'] * 1.016): return (True, 'signal_profit_q_pmax_bear') return (False, None) def exit_ichi(self, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, trade: 'Trade', current_time: 'datetime') -> tuple: if 0.0 < current_profit < 0.05 and current_time - timedelta(minutes=1440) > trade.open_date_utc and (last_candle['rsi_14'] > 78.0): return (True, 'signal_profit_ichi_u') elif -0.03 < current_profit < -0.0 and current_time - timedelta(minutes=1440) > trade.open_date_utc and (last_candle['rsi_14'] > 75.0): return (True, 'signal_stoploss_ichi_u') elif max_loss > 0.07 and current_profit > 0.02: return (True, 'signal_profit_ichi_r_0') elif max_loss > 0.06 and current_profit > 0.03: return (True, 'signal_profit_ichi_r_1') elif max_loss > 0.05 and current_profit > 0.04: return (True, 'signal_profit_ichi_r_2') elif max_loss > 0.04 and current_profit > 0.05: return (True, 'signal_profit_ichi_r_3') elif max_loss > 0.03 and current_profit > 0.06: return (True, 'signal_profit_ichi_r_4') elif 0.05 < current_profit < 0.1 and current_time - timedelta(minutes=720) > trade.open_date_utc: return (True, 'signal_profit_ichi_slow') elif 0.07 < current_profit < 0.1 and max_profit - current_profit > 0.025 and (max_profit > 0.1): return (True, 'signal_profit_ichi_t') elif current_profit < -0.1: return (True, 'signal_stoploss_ichi') return (False, None) def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] previous_candle_1 = dataframe.iloc[-2] previous_candle_2 = dataframe.iloc[-3] previous_candle_3 = dataframe.iloc[-4] previous_candle_4 = dataframe.iloc[-5] previous_candle_5 = dataframe.iloc[-6] entry_tag = 'empty' if hasattr(trade, 'entry_tag') and trade.entry_tag is not None: entry_tag = trade.entry_tag else: trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) entry_signal = dataframe.loc[dataframe['date'] < trade_open_date] if not entry_signal.empty: entry_signal_candle = entry_signal.iloc[-1] entry_tag = entry_signal_candle['entry_tag'] if entry_signal_candle['entry_tag'] != '' else 'empty' entry_tags = entry_tag.split() max_profit = (trade.max_rate - trade.open_rate) / trade.open_rate max_loss = (trade.open_rate - trade.min_rate) / trade.min_rate # Quick exit mode if all((c in ['32', '33', '34', '35', '36', '37', '38', '39', '40'] for c in entry_tags)): exit, signal_name = self.exit_quick_mode(current_profit, max_profit, last_candle, previous_candle_1) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # Ichi Trade management if all((c in ['39'] for c in entry_tags)): exit, signal_name = self.exit_ichi(current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # Over EMA200, main profit targets exit, signal_name = self.exit_over_main(current_profit, last_candle) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # Under EMA200, main profit targets exit, signal_name = self.exit_under_main(current_profit, last_candle) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # The pair is pumped exit, signal_name = self.exit_pump_main(current_profit, last_candle) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # The pair is descending exit, signal_name = self.exit_dec_main(current_profit, last_candle) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # Trailing exit, signal_name = self.exit_trail_main(current_profit, last_candle, max_profit) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # Duration based exit, signal_name = self.exit_duration_main(current_profit, last_candle, trade, current_time) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # Under EMA200, exit with any profit exit, signal_name = self.exit_under_min(current_profit, last_candle) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # Stoplosses exit, signal_name = self.exit_stoploss(current_profit, last_candle, previous_candle_1) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # Pumped descending pairs exit, signal_name = self.exit_pump_dec(current_profit, last_candle) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # Extra exits for pumped pairs exit, signal_name = self.exit_pump_extra(current_profit, last_candle, max_profit) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # Extra exits for trades that recovered exit, signal_name = self.exit_recover(current_profit, last_candle, max_loss) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # Williams %R based exit 1 exit, signal_name = self.exit_r_1(current_profit, last_candle) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # Williams %R based exit 2 exit, signal_name = self.exit_r_2(current_profit, last_candle) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # Williams %R based exit 3 exit, signal_name = self.exit_r_3(current_profit, last_candle) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # Williams %R based exit 4, plus CTI exit, signal_name = self.exit_r_4(current_profit, last_candle) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' # Profit Target Signal exit, signal_name = self.exit_profit_target(pair, trade, current_time, current_rate, current_profit, max_profit, last_candle, previous_candle_1) if exit and signal_name is not None: return signal_name + ' ( ' + entry_tag + ')' self.mark_profit_target(pair, trade, current_rate, current_profit, max_profit, last_candle, previous_candle_1) # Sell signal 1 if self.exit_condition_1_enable and last_candle['rsi_14'] > self.exit_rsi_bb_1 and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']) and (previous_candle_3['close'] > previous_candle_3['bb20_2_upp']) and (previous_candle_4['close'] > previous_candle_4['bb20_2_upp']) and (previous_candle_5['close'] > previous_candle_5['bb20_2_upp']): if last_candle['close'] > last_candle['ema_200']: if current_profit > 0.0: return 'exit_signal_1_1_1' + ' ( ' + entry_tag + ')' elif current_profit > 0.0: return 'exit_signal_1_2_1' + ' ( ' + entry_tag + ')' elif max_loss > 0.25: return 'exit_signal_1_2_2' + ' ( ' + entry_tag + ')' # Sell signal 2 elif self.exit_condition_2_enable and last_candle['rsi_14'] > self.exit_rsi_bb_2 and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']): if last_candle['close'] > last_candle['ema_200']: if current_profit > 0.0: return 'exit_signal_2_1_1' + ' ( ' + entry_tag + ')' elif current_profit > 0.0: return 'exit_signal_2_2_1' + ' ( ' + entry_tag + ')' elif max_loss > 0.25: return 'exit_signal_2_2_2' + ' ( ' + entry_tag + ')' # Sell signal 4 elif self.exit_condition_4_enable and last_candle['rsi_14'] > self.exit_dual_rsi_rsi_4 and (last_candle['rsi_14_1h'] > self.exit_dual_rsi_rsi_1h_4): if last_candle['close'] > last_candle['ema_200']: if current_profit > 0.0: return 'exit_signal_4_1_1' + ' ( ' + entry_tag + ')' elif current_profit > 0.0: return 'exit_signal_4_2_1' + ' ( ' + entry_tag + ')' elif max_loss > 0.25: return 'exit_signal_4_2_2' + ' ( ' + entry_tag + ')' # Sell signal 6 elif self.exit_condition_6_enable and last_candle['close'] < last_candle['ema_200'] and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > self.exit_rsi_under_6): if current_profit > 0.0: return 'exit_signal_6_1' + ' ( ' + entry_tag + ')' elif max_loss > 0.25: return 'exit_signal_6_2' + ' ( ' + entry_tag + ')' # Sell signal 7 elif self.exit_condition_7_enable and last_candle['rsi_14_1h'] > self.exit_rsi_1h_7 and last_candle['crossed_below_ema_12_26']: if last_candle['close'] > last_candle['ema_200']: if current_profit > 0.0: return 'exit_signal_7_1_1' + ' ( ' + entry_tag + ')' elif current_profit > 0.0: return 'exit_signal_7_2_1' + ' ( ' + entry_tag + ')' elif max_loss > 0.25: return 'exit_signal_7_2_2' + ' ( ' + entry_tag + ')' # Sell signal 8 elif self.exit_condition_8_enable and last_candle['close'] > last_candle['bb20_2_upp_1h'] * self.exit_bb_relative_8: if last_candle['close'] > last_candle['ema_200']: if current_profit > 0.0: return 'exit_signal_8_1_1' + ' ( ' + entry_tag + ')' elif current_profit > 0.0: return 'exit_signal_8_2_1' + ' ( ' + entry_tag + ')' elif max_loss > 0.25: return 'exit_signal_8_2_2' + ' ( ' + entry_tag + ')' return None def range_percent_change(self, dataframe: DataFrame, method, length: int) -> float: """ Rolling Percentage Change Maximum across interval. :param dataframe: DataFrame The original OHLC dataframe :param method: High to Low / Open to Close :param length: int The length to look back """ if method == 'HL': return (dataframe['high'].rolling(length).max() - dataframe['low'].rolling(length).min()) / dataframe['low'].rolling(length).min() elif method == 'OC': return (dataframe['open'].rolling(length).max() - dataframe['close'].rolling(length).min()) / dataframe['close'].rolling(length).min() else: raise ValueError(f'Method {method} not defined!') def top_percent_change(self, dataframe: DataFrame, length: int) -> float: """ Percentage change of the current close from the range maximum Open price :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ if length == 0: return (dataframe['open'] - dataframe['close']) / dataframe['close'] else: return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close'] def range_maxgap(self, dataframe: DataFrame, length: int) -> float: """ Maximum Price Gap across interval. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ return dataframe['open'].rolling(length).max() - dataframe['close'].rolling(length).min() def range_maxgap_adjusted(self, dataframe: DataFrame, length: int, adjustment: float) -> float: """ Maximum Price Gap across interval adjusted. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back :param adjustment: int The adjustment to be applied """ return self.range_maxgap(dataframe, length) / adjustment def range_height(self, dataframe: DataFrame, length: int) -> float: """ Current close distance to range bottom. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ return dataframe['close'] - dataframe['close'].rolling(length).min() def safe_pump(self, dataframe: DataFrame, length: int, thresh: float, pull_thresh: float) -> bool: """ Determine if entry after a pump is safe. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back :param thresh: int Maximum percentage change threshold :param pull_thresh: int Pullback from interval maximum threshold """ return (dataframe[f'oc_pct_change_{length}'] < thresh) | (self.range_maxgap_adjusted(dataframe, length, pull_thresh) > self.range_height(dataframe, length)) def safe_dips(self, dataframe: DataFrame, thresh_0, thresh_2, thresh_12, thresh_144) -> bool: """ Determine if dip is safe to enter. :param dataframe: DataFrame The original OHLC dataframe :param thresh_0: Threshold value for 0 length top pct change :param thresh_2: Threshold value for 2 length top pct change :param thresh_12: Threshold value for 12 length top pct change :param thresh_144: Threshold value for 144 length top pct change """ return (dataframe['tpct_change_0'] < thresh_0) & (dataframe['tpct_change_2'] < thresh_2) & (dataframe['tpct_change_12'] < thresh_12) & (dataframe['tpct_change_144'] < thresh_144) def informative_pairs(self): # get access to all pairs available in whitelist. pairs = self.dp.current_whitelist() # Assign tf to each pair so they can be downloaded and cached for strategy. informative_pairs = [(pair, self.info_timeframe) for pair in pairs] informative_pairs.append(('BTC/USDT', self.timeframe)) informative_pairs.append(('BTC/USDT', self.info_timeframe)) return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, 'DataProvider is required for multiple timeframes.' # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.info_timeframe) # EMA informative_1h['ema_12'] = ta.EMA(informative_1h, timeperiod=12) informative_1h['ema_15'] = ta.EMA(informative_1h, timeperiod=15) informative_1h['ema_20'] = ta.EMA(informative_1h, timeperiod=20) informative_1h['ema_25'] = ta.EMA(informative_1h, timeperiod=25) informative_1h['ema_26'] = ta.EMA(informative_1h, timeperiod=26) informative_1h['ema_35'] = ta.EMA(informative_1h, timeperiod=35) informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # SMA informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200) informative_1h['sma_200_dec_20'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(20) # RSI informative_1h['rsi_14'] = ta.RSI(informative_1h, timeperiod=14) # BB bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2) informative_1h['bb20_2_low'] = bollinger['lower'] informative_1h['bb20_2_mid'] = bollinger['mid'] informative_1h['bb20_2_upp'] = bollinger['upper'] # Chaikin Money Flow informative_1h['cmf'] = chaikin_money_flow(informative_1h, 20) # Williams %R informative_1h['r_480'] = williams_r(informative_1h, period=480) # CTI informative_1h['cti'] = pta.cti(informative_1h['close'], length=20) # Ichimoku ichi = ichimoku(informative_1h, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30) informative_1h['chikou_span'] = ichi['chikou_span'] informative_1h['tenkan_sen'] = ichi['tenkan_sen'] informative_1h['kijun_sen'] = ichi['kijun_sen'] informative_1h['senkou_a'] = ichi['senkou_span_a'] informative_1h['senkou_b'] = ichi['senkou_span_b'] informative_1h['leading_senkou_span_a'] = ichi['leading_senkou_span_a'] informative_1h['leading_senkou_span_b'] = ichi['leading_senkou_span_b'] informative_1h['chikou_span_greater'] = (informative_1h['chikou_span'] > informative_1h['senkou_a']).shift(30).fillna(False) informative_1h.loc[:, 'cloud_top'] = informative_1h.loc[:, ['senkou_a', 'senkou_b']].max(axis=1) # EFI - Elders Force Index informative_1h['efi'] = pta.efi(informative_1h['close'], informative_1h['volume'], length=13) # SSL ssl_down, ssl_up = SSLChannels(informative_1h, 10) informative_1h['ssl_down'] = ssl_down informative_1h['ssl_up'] = ssl_up # Pump protections informative_1h['hl_pct_change_48'] = self.range_percent_change(informative_1h, 'HL', 48) informative_1h['hl_pct_change_36'] = self.range_percent_change(informative_1h, 'HL', 36) informative_1h['hl_pct_change_24'] = self.range_percent_change(informative_1h, 'HL', 24) informative_1h['oc_pct_change_48'] = self.range_percent_change(informative_1h, 'OC', 48) informative_1h['oc_pct_change_36'] = self.range_percent_change(informative_1h, 'OC', 36) informative_1h['oc_pct_change_24'] = self.range_percent_change(informative_1h, 'OC', 24) informative_1h['hl_pct_change_5'] = self.range_percent_change(informative_1h, 'HL', 5) informative_1h['low_5'] = informative_1h['low'].shift().rolling(5).min() informative_1h['safe_pump_24_10'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_10_24, self.entry_pump_pull_threshold_10_24) informative_1h['safe_pump_36_10'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_10_36, self.entry_pump_pull_threshold_10_36) informative_1h['safe_pump_48_10'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_10_48, self.entry_pump_pull_threshold_10_48) informative_1h['safe_pump_24_20'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_20_24, self.entry_pump_pull_threshold_20_24) informative_1h['safe_pump_36_20'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_20_36, self.entry_pump_pull_threshold_20_36) informative_1h['safe_pump_48_20'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_20_48, self.entry_pump_pull_threshold_20_48) informative_1h['safe_pump_24_30'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_30_24, self.entry_pump_pull_threshold_30_24) informative_1h['safe_pump_36_30'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_30_36, self.entry_pump_pull_threshold_30_36) informative_1h['safe_pump_48_30'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_30_48, self.entry_pump_pull_threshold_30_48) informative_1h['safe_pump_24_40'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_40_24, self.entry_pump_pull_threshold_40_24) informative_1h['safe_pump_36_40'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_40_36, self.entry_pump_pull_threshold_40_36) informative_1h['safe_pump_48_40'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_40_48, self.entry_pump_pull_threshold_40_48) informative_1h['safe_pump_24_50'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_50_24, self.entry_pump_pull_threshold_50_24) informative_1h['safe_pump_36_50'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_50_36, self.entry_pump_pull_threshold_50_36) informative_1h['safe_pump_48_50'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_50_48, self.entry_pump_pull_threshold_50_48) informative_1h['safe_pump_24_60'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_60_24, self.entry_pump_pull_threshold_60_24) informative_1h['safe_pump_36_60'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_60_36, self.entry_pump_pull_threshold_60_36) informative_1h['safe_pump_48_60'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_60_48, self.entry_pump_pull_threshold_60_48) informative_1h['safe_pump_24_70'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_70_24, self.entry_pump_pull_threshold_70_24) informative_1h['safe_pump_36_70'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_70_36, self.entry_pump_pull_threshold_70_36) informative_1h['safe_pump_48_70'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_70_48, self.entry_pump_pull_threshold_70_48) informative_1h['safe_pump_24_80'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_80_24, self.entry_pump_pull_threshold_80_24) informative_1h['safe_pump_36_80'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_80_36, self.entry_pump_pull_threshold_80_36) informative_1h['safe_pump_48_80'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_80_48, self.entry_pump_pull_threshold_80_48) informative_1h['safe_pump_24_90'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_90_24, self.entry_pump_pull_threshold_90_24) informative_1h['safe_pump_36_90'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_90_36, self.entry_pump_pull_threshold_90_36) informative_1h['safe_pump_48_90'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_90_48, self.entry_pump_pull_threshold_90_48) informative_1h['safe_pump_24_100'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_100_24, self.entry_pump_pull_threshold_100_24) informative_1h['safe_pump_36_100'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_100_36, self.entry_pump_pull_threshold_100_36) informative_1h['safe_pump_48_100'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_100_48, self.entry_pump_pull_threshold_100_48) informative_1h['safe_pump_24_110'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_110_24, self.entry_pump_pull_threshold_110_24) informative_1h['safe_pump_36_110'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_110_36, self.entry_pump_pull_threshold_110_36) informative_1h['safe_pump_48_110'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_110_48, self.entry_pump_pull_threshold_110_48) informative_1h['safe_pump_24_120'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_120_24, self.entry_pump_pull_threshold_120_24) informative_1h['safe_pump_36_120'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_120_36, self.entry_pump_pull_threshold_120_36) informative_1h['safe_pump_48_120'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_120_48, self.entry_pump_pull_threshold_120_48) informative_1h['safe_dump_10'] = (informative_1h['hl_pct_change_5'] < self.entry_dump_protection_10_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open']) informative_1h['safe_dump_20'] = (informative_1h['hl_pct_change_5'] < self.entry_dump_protection_20_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open']) informative_1h['safe_dump_30'] = (informative_1h['hl_pct_change_5'] < self.entry_dump_protection_30_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open']) informative_1h['safe_dump_40'] = (informative_1h['hl_pct_change_5'] < self.entry_dump_protection_40_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open']) informative_1h['safe_dump_50'] = (informative_1h['hl_pct_change_5'] < self.entry_dump_protection_50_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open']) informative_1h['safe_dump_60'] = (informative_1h['hl_pct_change_5'] < self.entry_dump_protection_60_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open']) informative_1h['exit_pump_48_1'] = informative_1h['hl_pct_change_48'] > self.exit_pump_threshold_48_1 informative_1h['exit_pump_48_2'] = informative_1h['hl_pct_change_48'] > self.exit_pump_threshold_48_2 informative_1h['exit_pump_48_3'] = informative_1h['hl_pct_change_48'] > self.exit_pump_threshold_48_3 informative_1h['exit_pump_36_1'] = informative_1h['hl_pct_change_36'] > self.exit_pump_threshold_36_1 informative_1h['exit_pump_36_2'] = informative_1h['hl_pct_change_36'] > self.exit_pump_threshold_36_2 informative_1h['exit_pump_36_3'] = informative_1h['hl_pct_change_36'] > self.exit_pump_threshold_36_3 informative_1h['exit_pump_24_1'] = informative_1h['hl_pct_change_24'] > self.exit_pump_threshold_24_1 informative_1h['exit_pump_24_2'] = informative_1h['hl_pct_change_24'] > self.exit_pump_threshold_24_2 informative_1h['exit_pump_24_3'] = informative_1h['hl_pct_change_24'] > self.exit_pump_threshold_24_3 return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # BB 40 - STD2 bb_40_std2 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['bb40_2_low'] = bb_40_std2['lower'] dataframe['bb40_2_mid'] = bb_40_std2['mid'] dataframe['bb40_2_delta'] = (bb_40_std2['mid'] - dataframe['bb40_2_low']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['bb40_2_low']).abs() # BB 20 - STD2 bb_20_std2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb20_2_low'] = bb_20_std2['lower'] dataframe['bb20_2_mid'] = bb_20_std2['mid'] dataframe['bb20_2_upp'] = bb_20_std2['upper'] # EMA 200 dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13) dataframe['ema_15'] = ta.EMA(dataframe, timeperiod=15) dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema_25'] = ta.EMA(dataframe, timeperiod=25) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_35'] = ta.EMA(dataframe, timeperiod=35) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) # SMA dataframe['sma_5'] = ta.SMA(dataframe, timeperiod=5) dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['sma_20'] = ta.SMA(dataframe, timeperiod=20) dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec_20'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) dataframe['sma_200_dec_24'] = dataframe['sma_200'] < dataframe['sma_200'].shift(24) # MFI dataframe['mfi'] = ta.MFI(dataframe) # CMF dataframe['cmf'] = chaikin_money_flow(dataframe, 20) # EWO dataframe['ewo'] = ewo(dataframe, 50, 200) # RSI dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_20'] = ta.RSI(dataframe, timeperiod=20) # Chopiness dataframe['chop'] = qtpylib.chopiness(dataframe, 14) # Zero-Lag EMA dataframe['zema_61'] = zema(dataframe, period=61) # Williams %R dataframe['r_480'] = williams_r(dataframe, period=480) # Stochastic RSI stochrsi = ta.STOCHRSI(dataframe, timeperiod=96, fastk_period=3, fastd_period=3, fastd_matype=0) dataframe['stochrsi_fastk_96'] = stochrsi['fastk'] dataframe['stochrsi_fastd_96'] = stochrsi['fastd'] # Modified Elder Ray Index dataframe['moderi_32'] = moderi(dataframe, 32) dataframe['moderi_64'] = moderi(dataframe, 64) dataframe['moderi_96'] = moderi(dataframe, 96) # hull dataframe['hull_75'] = hull(dataframe, 75) # zlema dataframe['zlema_68'] = zlema(dataframe, 68) # CTI dataframe['cti'] = pta.cti(dataframe['close'], length=20) # For exit checks dataframe['crossed_below_ema_12_26'] = qtpylib.crossed_below(dataframe['ema_12'], dataframe['ema_26']) # Heiken Ashi heikinashi = qtpylib.heikinashi(dataframe) heikinashi['volume'] = dataframe['volume'] # Profit Maximizer - PMAX dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3) dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close']) / 4 dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9) dataframe['sma_21'] = ta.SMA(dataframe, timeperiod=21) dataframe['sma_68'] = ta.SMA(dataframe, timeperiod=68) dataframe['sma_75'] = ta.SMA(dataframe, timeperiod=75) # HLC3 dataframe['hlc3'] = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3 # HRSI dataframe['hull'] = 2 * dataframe['hlc3'] - ta.WMA(dataframe['hlc3'], 2) dataframe['hrsi'] = ta.RSI(dataframe['hull'], 2) # ZLEMA dataframe['zlema_2'] = pta.zlma(dataframe['hlc3'], length=2) dataframe['zlema_4'] = pta.zlma(dataframe['hlc3'], length=4) # CCI dataframe['cci'] = ta.CCI(dataframe, source='hlc3', timeperiod=20) # ATR dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['atr_high_thresh_1'] = dataframe['high'] - dataframe['atr'] * 5.4 dataframe['atr_high_thresh_2'] = dataframe['high'] - dataframe['atr'] * 5.2 dataframe['atr_high_thresh_3'] = dataframe['high'] - dataframe['atr'] * 5.0 dataframe['atr_high_thresh_4'] = dataframe['high'] - dataframe['atr'] * 2.0 dataframe['atr_high_thresh_q'] = dataframe['high'] - dataframe['atr'] * 3.0 # Dip protection dataframe['tpct_change_0'] = self.top_percent_change(dataframe, 0) dataframe['tpct_change_2'] = self.top_percent_change(dataframe, 2) dataframe['tpct_change_12'] = self.top_percent_change(dataframe, 12) dataframe['tpct_change_144'] = self.top_percent_change(dataframe, 144) dataframe['safe_dips_10'] = self.safe_dips(dataframe, self.entry_dip_threshold_10_1, self.entry_dip_threshold_10_2, self.entry_dip_threshold_10_3, self.entry_dip_threshold_10_4) dataframe['safe_dips_20'] = self.safe_dips(dataframe, self.entry_dip_threshold_20_1, self.entry_dip_threshold_20_2, self.entry_dip_threshold_20_3, self.entry_dip_threshold_20_4) dataframe['safe_dips_30'] = self.safe_dips(dataframe, self.entry_dip_threshold_30_1, self.entry_dip_threshold_30_2, self.entry_dip_threshold_30_3, self.entry_dip_threshold_30_4) dataframe['safe_dips_40'] = self.safe_dips(dataframe, self.entry_dip_threshold_40_1, self.entry_dip_threshold_40_2, self.entry_dip_threshold_40_3, self.entry_dip_threshold_40_4) dataframe['safe_dips_50'] = self.safe_dips(dataframe, self.entry_dip_threshold_50_1, self.entry_dip_threshold_50_2, self.entry_dip_threshold_50_3, self.entry_dip_threshold_50_4) dataframe['safe_dips_60'] = self.safe_dips(dataframe, self.entry_dip_threshold_60_1, self.entry_dip_threshold_60_2, self.entry_dip_threshold_60_3, self.entry_dip_threshold_60_4) dataframe['safe_dips_70'] = self.safe_dips(dataframe, self.entry_dip_threshold_70_1, self.entry_dip_threshold_70_2, self.entry_dip_threshold_70_3, self.entry_dip_threshold_70_4) dataframe['safe_dips_80'] = self.safe_dips(dataframe, self.entry_dip_threshold_80_1, self.entry_dip_threshold_80_2, self.entry_dip_threshold_80_3, self.entry_dip_threshold_80_4) dataframe['safe_dips_90'] = self.safe_dips(dataframe, self.entry_dip_threshold_90_1, self.entry_dip_threshold_90_2, self.entry_dip_threshold_90_3, self.entry_dip_threshold_90_4) dataframe['safe_dips_100'] = self.safe_dips(dataframe, self.entry_dip_threshold_100_1, self.entry_dip_threshold_100_2, self.entry_dip_threshold_100_3, self.entry_dip_threshold_100_4) dataframe['safe_dips_110'] = self.safe_dips(dataframe, self.entry_dip_threshold_110_1, self.entry_dip_threshold_110_2, self.entry_dip_threshold_110_3, self.entry_dip_threshold_110_4) dataframe['safe_dips_120'] = self.safe_dips(dataframe, self.entry_dip_threshold_120_1, self.entry_dip_threshold_120_2, self.entry_dip_threshold_120_3, self.entry_dip_threshold_120_4) dataframe['safe_dips_130'] = self.safe_dips(dataframe, self.entry_dip_threshold_130_1, self.entry_dip_threshold_130_2, self.entry_dip_threshold_130_3, self.entry_dip_threshold_130_4) # Volume dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1) dataframe['volume_mean_30'] = dataframe['volume'].rolling(30).mean() #HLC3 dataframe['hlc3'] = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3 #HRSI dataframe['hull'] = 2 * dataframe['hlc3'] - ta.WMA(dataframe['hlc3'], 2) dataframe['hrsi'] = ta.RSI(dataframe['hull'], 2) dataframe['hull_exit'] = 2 * dataframe['low'] - ta.WMA(dataframe['low'], 2) dataframe['hrsi_exit'] = ta.RSI(dataframe['hull_exit'], 2) #Kalman Filter HLC3 dataframe['hlc3KF'] = KalmanFilter(dataframe, source='hlc3') #Kalman Filter LOW dataframe['lowKF'] = KalmanFilter(dataframe, source='low') #ZLEMA BUY dataframe['zlema_1'] = dataframe['hlc3KF'] dataframe['zlema_1_std'] = dataframe['hlc3'] dataframe['ema_data'] = dataframe['hlc3KF'] + (dataframe['hlc3KF'] - dataframe['hlc3KF'].shift(2)) dataframe['ema_data_2'] = dataframe['hlc3KF'] + (dataframe['hlc3KF'] - dataframe['hlc3KF'].shift(1)) dataframe['zlema_4'] = ta.EMA(dataframe['ema_data'], timeperiod=4) dataframe['zlema_2'] = ta.EMA(dataframe['ema_data_2'], timeperiod=2) dataframe['zlema_4_std'] = pta.zlma(dataframe['hlc3'], length=4) #ZLEMA SELL dataframe['zlema_1_exit'] = dataframe['lowKF'] dataframe['zlema_1_std_exit'] = dataframe['low'] dataframe['ema_data_exit'] = dataframe['lowKF'] + (dataframe['lowKF'] - dataframe['lowKF'].shift(2)) dataframe['zlema_4_exit'] = ta.EMA(dataframe['ema_data_exit'], timeperiod=4) dataframe['zlema_4_std_exit'] = pta.zlma(dataframe['low'], length=4) #CCI dataframe['cci'] = ta.CCI(dataframe, source='hlc3', timeperiod=20) if not self.config['runmode'].value in ('live', 'dry_run'): # Backtest age filter dataframe['bt_agefilter_ok'] = False dataframe.loc[dataframe.index > 12 * 24 * self.bt_min_age_days, 'bt_agefilter_ok'] = True else: # Exchange downtime protection dataframe['live_data_ok'] = dataframe['volume'].rolling(window=72, min_periods=72).min() > 0 return dataframe def resampled_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Indicators # ----------------------------------------------------------------------------------------- dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) return dataframe def base_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Indicators # ----------------------------------------------------------------------------------------- dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume'] dataframe.rename(columns=lambda s: 'btc_' + s if not s in ignore_columns else s, inplace=True) return dataframe def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Indicators # ----------------------------------------------------------------------------------------- dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['not_downtrend'] = (dataframe['close'] > dataframe['close'].shift(2)) | (dataframe['rsi_14'] > 50) # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume'] dataframe.rename(columns=lambda s: 'btc_' + s if not s in ignore_columns else s, inplace=True) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ --> BTC informative (5m/1h) ___________________________________________________________________________________________ """ if self.has_BTC_base_tf: btc_base_tf = self.dp.get_pair_dataframe('BTC/USDT', self.timeframe) btc_base_tf = self.base_tf_btc_indicators(btc_base_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_base_tf, self.timeframe, self.timeframe, ffill=True) drop_columns = [s + '_' + self.timeframe for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) if self.has_BTC_info_tf: btc_info_tf = self.dp.get_pair_dataframe('BTC/USDT', self.info_timeframe) btc_info_tf = self.info_tf_btc_indicators(btc_info_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_info_tf, self.timeframe, self.info_timeframe, ffill=True) drop_columns = [s + '_' + self.info_timeframe for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) '\n --> Informative timeframe\n ___________________________________________________________________________________________\n ' if self.info_timeframe != 'none': informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.info_timeframe, ffill=True) drop_columns = [s + '_' + self.info_timeframe for s in ['date']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) '\n --> Resampled to another timeframe\n ___________________________________________________________________________________________\n ' if self.res_timeframe != 'none': resampled = resample_to_interval(dataframe, timeframe_to_minutes(self.res_timeframe)) resampled = self.resampled_tf_indicators(resampled, metadata) # Merge resampled info dataframe dataframe = resampled_merge(dataframe, resampled, fill_na=True) dataframe.rename(columns=lambda s: s + '_{}'.format(self.res_timeframe) if 'resample_' in s else s, inplace=True) dataframe.rename(columns=lambda s: s.replace('resample_{}_'.format(self.res_timeframe.replace('m', '')), ''), inplace=True) drop_columns = [s + '_' + self.res_timeframe for s in ['date']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) '\n --> The indicators for the normal (5m) timeframe\n ___________________________________________________________________________________________\n ' dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'entry_tag'] = '' for index in self.entry_protection_params: item_entry_protection_list = [True] global_entry_protection_params = self.entry_protection_params[index] if self.entry_params['entry_condition_' + str(index) + '_enable']: # Standard protections - Common to every condition # ----------------------------------------------------------------------------------------- if global_entry_protection_params['ema_fast']: item_entry_protection_list.append(dataframe[f"ema_{global_entry_protection_params['ema_fast_len']}"] > dataframe['ema_200']) if global_entry_protection_params['ema_slow']: item_entry_protection_list.append(dataframe[f"ema_{global_entry_protection_params['ema_slow_len']}_1h"] > dataframe['ema_200_1h']) if global_entry_protection_params['close_above_ema_fast']: item_entry_protection_list.append(dataframe['close'] > dataframe[f"ema_{global_entry_protection_params['close_above_ema_fast_len']}"]) if global_entry_protection_params['close_above_ema_slow']: item_entry_protection_list.append(dataframe['close'] > dataframe[f"ema_{global_entry_protection_params['close_above_ema_slow_len']}_1h"]) if global_entry_protection_params['sma200_rising']: item_entry_protection_list.append(dataframe['sma_200'] > dataframe['sma_200'].shift(int(global_entry_protection_params['sma200_rising_val']))) if global_entry_protection_params['sma200_1h_rising']: item_entry_protection_list.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(int(global_entry_protection_params['sma200_1h_rising_val']))) if global_entry_protection_params['safe_dips']: item_entry_protection_list.append(dataframe[f"safe_dips_{global_entry_protection_params['safe_dips_type']}"]) if global_entry_protection_params['safe_pump']: item_entry_protection_list.append(dataframe[f"safe_pump_{global_entry_protection_params['safe_pump_period']}_{global_entry_protection_params['safe_pump_type']}_1h"]) if global_entry_protection_params['btc_1h_not_downtrend']: item_entry_protection_list.append(dataframe['btc_not_downtrend_1h']) if not self.config['runmode'].value in ('live', 'dry_run'): if self.has_bt_agefilter: item_entry_protection_list.append(dataframe['bt_agefilter_ok']) elif self.has_downtime_protection: item_entry_protection_list.append(dataframe['live_data_ok']) # Buy conditions # ----------------------------------------------------------------------------------------- item_entry_logic = [] item_entry_logic.append(reduce(lambda x, y: x & y, item_entry_protection_list)) # Condition #1 if index == 1: # Non-Standard protections # Logic item_entry_logic.append((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min() > self.entry_min_inc_1) item_entry_logic.append(dataframe['rsi_14_1h'] > self.entry_rsi_1h_min_1) item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_max_1) item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_1) item_entry_logic.append(dataframe['mfi'] < self.entry_mfi_1) item_entry_logic.append(dataframe['cti'] < self.entry_cti_1) # Condition #2 elif index == 2: # Non-Standard protections # Logic item_entry_logic.append(dataframe['rsi_14'] < dataframe['rsi_14_1h'] - self.entry_rsi_1h_diff_2) item_entry_logic.append(dataframe['mfi'] < self.entry_mfi_2) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_2) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_volume_2) # Condition #3 elif index == 3: # Non-Standard protections item_entry_logic.append(dataframe['close'] > dataframe['ema_200_1h'] * self.entry_ema_rel_3) # Logic item_entry_logic.append(dataframe['bb40_2_low'].shift().gt(0)) item_entry_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * self.entry_bb40_bbdelta_close_3)) item_entry_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.entry_bb40_closedelta_close_3)) item_entry_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.entry_bb40_tail_bbdelta_3)) item_entry_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift())) item_entry_logic.append(dataframe['close'].le(dataframe['close'].shift())) item_entry_logic.append(dataframe['cti'] < self.entry_cti_3) # Condition #4 elif index == 4: # Non-Standard protections # Logic item_entry_logic.append(dataframe['close'] < dataframe['ema_50']) item_entry_logic.append(dataframe['close'] < self.entry_bb20_close_bblowerband_4 * dataframe['bb20_2_low']) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_30'].shift(1) * self.entry_bb20_volume_4) item_entry_logic.append(dataframe['cti'] < self.entry_cti_4) # Condition #5 elif index == 5: # Non-Standard protections item_entry_logic.append(dataframe['close'] > dataframe['ema_200_1h'] * self.entry_ema_rel_5) # Logic item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_open_mult_5) item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_5) item_entry_logic.append(dataframe['cti'] < self.entry_cti_5) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_volume_5) # Condition #6 elif index == 6: # Non-Standard protections # Logic item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_open_mult_6) item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_6) # Condition #7 elif index == 7: # Non-Standard protections # Logic item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_open_mult_7) item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) item_entry_logic.append(dataframe['cti'] < self.entry_cti_7) # Condition #8 elif index == 8: # Non-Standard protections # Logic item_entry_logic.append(dataframe['moderi_96']) item_entry_logic.append(dataframe['cti'] < self.entry_cti_8) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_8) item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_8) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_volume_8) # Condition #9 elif index == 9: # Non-Standard protections item_entry_logic.append(dataframe['ema_50'] > dataframe['ema_200']) # Logic item_entry_logic.append(dataframe['close'] < dataframe['ema_20'] * self.entry_ma_offset_9) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_9) item_entry_logic.append(dataframe['rsi_14_1h'] > self.entry_rsi_1h_min_9) item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_max_9) item_entry_logic.append(dataframe['mfi'] < self.entry_mfi_9) # Condition #10 elif index == 10: # Non-Standard protections item_entry_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h']) # Logic item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_ma_offset_10) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_10) item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_10) # Condition #11 elif index == 11: # Non-Standard protections item_entry_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h']) # Logic item_entry_logic.append((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min() > self.entry_min_inc_11) item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_ma_offset_11) item_entry_logic.append(dataframe['rsi_14_1h'] > self.entry_rsi_1h_min_11) item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_max_11) item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_11) item_entry_logic.append(dataframe['mfi'] < self.entry_mfi_11) # Condition #12 elif index == 12: # Non-Standard protections # Logic item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_ma_offset_12) item_entry_logic.append(dataframe['ewo'] > self.entry_ewo_12) item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_12) item_entry_logic.append(dataframe['cti'] < self.entry_cti_12) # Condition #13 elif index == 13: # Non-Standard protections item_entry_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h']) # Logic item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_ma_offset_13) item_entry_logic.append(dataframe['cti'] < self.entry_cti_13) item_entry_logic.append(dataframe['ewo'] < self.entry_ewo_13) # Condition #14 elif index == 14: # Non-Standard protections # Logic item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_open_mult_14) item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_14) item_entry_logic.append(dataframe['close'] < dataframe['ema_20'] * self.entry_ma_offset_14) item_entry_logic.append(dataframe['cti'] < self.entry_cti_14) # Condition #15 elif index == 15: # Non-Standard protections item_entry_logic.append(dataframe['close'] > dataframe['ema_200_1h'] * self.entry_ema_rel_15) # Logic item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_open_mult_15) item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_15) item_entry_logic.append(dataframe['close'] < dataframe['ema_20'] * self.entry_ma_offset_15) # Condition #16 elif index == 16: # Non-Standard protections # Logic item_entry_logic.append(dataframe['close'] < dataframe['ema_20'] * self.entry_ma_offset_16) item_entry_logic.append(dataframe['ewo'] > self.entry_ewo_16) item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_16) item_entry_logic.append(dataframe['cti'] < self.entry_cti_16) # Condition #17 elif index == 17: # Non-Standard protections # Logic item_entry_logic.append(dataframe['close'] < dataframe['ema_20'] * self.entry_ma_offset_17) item_entry_logic.append(dataframe['ewo'] < self.entry_ewo_17) item_entry_logic.append(dataframe['cti'] < self.entry_cti_17) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_volume_17) # Condition #18 elif index == 18: # Non-Standard protections item_entry_logic.append(dataframe['sma_200'] > dataframe['sma_200'].shift(20)) item_entry_logic.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(36)) # Logic item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_18) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_18) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_volume_18) item_entry_logic.append(dataframe['cti'] < self.entry_cti_18) # Condition #19 elif index == 19: # Non-Standard protections # Logic item_entry_logic.append(dataframe['close'].shift(1) > dataframe['ema_100_1h']) item_entry_logic.append(dataframe['low'] < dataframe['ema_100_1h']) item_entry_logic.append(dataframe['close'] > dataframe['ema_100_1h']) item_entry_logic.append(dataframe['rsi_14_1h'] > self.entry_rsi_1h_min_19) item_entry_logic.append(dataframe['chop'] < self.entry_chop_max_19) item_entry_logic.append(dataframe['moderi_32'] == True) item_entry_logic.append(dataframe['moderi_64'] == True) item_entry_logic.append(dataframe['moderi_96'] == True) # Condition #20 elif index == 20: # Non-Standard protections # Logic item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_20) item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_20) item_entry_logic.append(dataframe['cti'] < self.entry_cti_20) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_volume_20) # Condition #21 elif index == 21: # Non-Standard protections # Logic item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_21) item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_21) item_entry_logic.append(dataframe['cti'] < self.entry_cti_21) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_volume_21) # Condition #22 elif index == 22: # Non-Standard protections item_entry_logic.append(dataframe['ema_100_1h'] > dataframe['ema_100_1h'].shift(12)) item_entry_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(36)) # Logic item_entry_logic.append(dataframe['volume_mean_4'] * self.entry_volume_22 > dataframe['volume']) item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_ma_offset_22) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_22) item_entry_logic.append(dataframe['ewo'] > self.entry_ewo_22) item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_22) # Condition #23 elif index == 23: # Non-Standard protections # Logic item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_23) item_entry_logic.append(dataframe['ewo'] > self.entry_ewo_23) item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_23) item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_23) # Condition #24 elif index == 24: # Non-Standard protections # Logic item_entry_logic.append(dataframe['ema_12_1h'].shift(12) < dataframe['ema_35_1h'].shift(12)) item_entry_logic.append(dataframe['ema_12_1h'] > dataframe['ema_35_1h']) item_entry_logic.append(dataframe['cmf_1h'].shift(12) < 0) item_entry_logic.append(dataframe['cmf_1h'] > 0) item_entry_logic.append(dataframe['rsi_14'] < self.entry_24_rsi_max) item_entry_logic.append(dataframe['rsi_14_1h'] > self.entry_24_rsi_1h_min) # Condition #25 elif index == 25: # Non-Standard protections # Logic item_entry_logic.append(dataframe['rsi_20'] < dataframe['rsi_20'].shift()) item_entry_logic.append(dataframe['rsi_4'] < self.entry_25_rsi_4) item_entry_logic.append(dataframe['ema_20_1h'] > dataframe['ema_26_1h']) item_entry_logic.append(dataframe['close'] < dataframe['sma_20'] * self.entry_25_ma_offset) item_entry_logic.append(dataframe['open'] > dataframe['sma_20'] * self.entry_25_ma_offset) item_entry_logic.append((dataframe['open'] < dataframe['ema_20_1h']) & (dataframe['low'] < dataframe['ema_20_1h']) | (dataframe['open'] > dataframe['ema_20_1h']) & (dataframe['low'] > dataframe['ema_20_1h'])) item_entry_logic.append(dataframe['cti'] < self.entry_25_cti) # Condition #26 elif index == 26: # Non-Standard protections item_entry_logic.append(dataframe['close'] < dataframe['sma_75']) # Logic item_entry_logic.append(dataframe['close'] < dataframe['zema_61'] * self.entry_26_zema_low_offset) item_entry_logic.append(dataframe['cti'] < self.entry_26_cti) item_entry_logic.append(dataframe['r_480'] > self.entry_26_r) item_entry_logic.append(dataframe['r_480_1h'] > self.entry_26_r_1h) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_26_volume) # Condition #27 elif index == 27: # Non-Standard protections # Logic item_entry_logic.append(dataframe['r_480'] < -self.entry_27_wr_max) item_entry_logic.append(dataframe['r_480_1h'] < -self.entry_27_wr_1h_max) item_entry_logic.append(dataframe['rsi_14_1h'] + dataframe['rsi_14'] < self.entry_27_rsi_max) item_entry_logic.append(dataframe['cti'] < self.entry_27_cti) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_27_volume) # Condition #28 elif index == 28: # Non-Standard protections # Logic item_entry_logic.append(dataframe['moderi_64'] == True) item_entry_logic.append(dataframe['close'] < dataframe['hull_75'] * self.entry_28_ma_offset) item_entry_logic.append(dataframe['ewo'] > self.entry_28_ewo) item_entry_logic.append(dataframe['rsi_14'] < self.entry_28_rsi) item_entry_logic.append(dataframe['cti'] < self.entry_28_cti) # Condition #29 elif index == 29: # Non-Standard protections # Logic item_entry_logic.append(dataframe['moderi_64'] == True) item_entry_logic.append(dataframe['close'] < dataframe['hull_75'] * self.entry_29_ma_offset) item_entry_logic.append(dataframe['ewo'] < self.entry_29_ewo) item_entry_logic.append(dataframe['cti'] < self.entry_29_cti) # Condition #30 elif index == 30: # Non-Standard protections # Logic item_entry_logic.append(dataframe['moderi_64'] == False) item_entry_logic.append(dataframe['close'] < dataframe['zlema_68'] * self.entry_30_ma_offset) item_entry_logic.append(dataframe['ewo'] > self.entry_30_ewo) item_entry_logic.append(dataframe['rsi_14'] < self.entry_30_rsi) item_entry_logic.append(dataframe['cti'] < self.entry_30_cti) # Condition #31 elif index == 31: # Non-Standard protections # Logic item_entry_logic.append(dataframe['moderi_64'] == False) item_entry_logic.append(dataframe['close'] < dataframe['zlema_68'] * self.entry_31_ma_offset) item_entry_logic.append(dataframe['ewo'] < self.entry_31_ewo) item_entry_logic.append(dataframe['r_480'] < self.entry_31_wr) # Condition #32 - Quick mode entry elif index == 32: # Non-Standard protections # Logic item_entry_logic.append(dataframe['moderi_32']) item_entry_logic.append(dataframe['moderi_64']) item_entry_logic.append(dataframe['moderi_96']) item_entry_logic.append(dataframe['cti'] < self.entry_32_cti) item_entry_logic.append(dataframe['rsi_20'] < dataframe['rsi_20'].shift(1)) item_entry_logic.append(dataframe['rsi_4'] < self.entry_32_rsi) item_entry_logic.append(dataframe['ema_20_1h'] > dataframe['ema_25_1h']) item_entry_logic.append((dataframe['open'] - dataframe['close']) / dataframe['close'] < self.entry_32_dip) item_entry_logic.append(dataframe['close'] < dataframe['sma_15'] * self.entry_32_ma_offset) item_entry_logic.append((dataframe['open'] < dataframe['ema_20_1h']) & (dataframe['low'] < dataframe['ema_20_1h']) | (dataframe['open'] > dataframe['ema_20_1h']) & (dataframe['low'] > dataframe['ema_20_1h'])) # Condition #33 - Quick mode entry elif index == 33: # Non-Standard protections # Logic item_entry_logic.append(dataframe['moderi_96']) item_entry_logic.append(dataframe['cti'] < self.entry_33_cti) item_entry_logic.append(dataframe['close'] < dataframe['ema_13'] * self.entry_33_ma_offset) item_entry_logic.append(dataframe['ewo'] > self.entry_33_ewo) item_entry_logic.append(dataframe['rsi_14'] < self.entry_33_rsi) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_33_volume) # Condition #34 - Quick mode entry elif index == 34: # Non-Standard protections # Logic item_entry_logic.append(dataframe['cti'] < self.entry_34_cti) item_entry_logic.append((dataframe['open'] - dataframe['close']) / dataframe['close'] < self.entry_34_dip) item_entry_logic.append(dataframe['close'] < dataframe['ema_13'] * self.entry_34_ma_offset) item_entry_logic.append(dataframe['ewo'] < self.entry_34_ewo) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_34_volume) # Condition #35 - PMAX0 entry elif index == 35: # Non-Standard protections # Logic item_entry_logic.append(dataframe['pm'] <= dataframe['pmax_thresh']) item_entry_logic.append(dataframe['close'] < dataframe['sma_75'] * self.entry_35_ma_offset) item_entry_logic.append(dataframe['ewo'] > self.entry_35_ewo) item_entry_logic.append(dataframe['rsi_14'] < self.entry_35_rsi) item_entry_logic.append(dataframe['cti'] < self.entry_35_cti) # Condition #36 - PMAX1 entry elif index == 36: # Non-Standard protections (add below) # Logic item_entry_logic.append(dataframe['pm'] <= dataframe['pmax_thresh']) item_entry_logic.append(dataframe['close'] < dataframe['sma_75'] * self.entry_36_ma_offset) item_entry_logic.append(dataframe['ewo'] < self.entry_36_ewo) item_entry_logic.append(dataframe['cti'] < self.entry_36_cti) # Condition #37 - PMAX2 entry elif index == 37: # Non-Standard protections (add below) # Logic item_entry_logic.append(dataframe['pm'] > dataframe['pmax_thresh']) item_entry_logic.append(dataframe['close'] < dataframe['sma_75'] * self.entry_37_ma_offset) item_entry_logic.append(dataframe['ewo'] > self.entry_37_ewo) item_entry_logic.append(dataframe['rsi_14'] < self.entry_37_rsi) item_entry_logic.append(dataframe['cti'] < self.entry_37_cti) item_entry_logic.append(dataframe['safe_dump_50_1h']) # Condition #38 - PMAX3 entry elif index == 38: # Non-Standard protections (add below) # Logic item_entry_logic.append(dataframe['pm'] > dataframe['pmax_thresh']) item_entry_logic.append(dataframe['close'] < dataframe['sma_75'] * self.entry_38_ma_offset) item_entry_logic.append(dataframe['ewo'] < self.entry_38_ewo) item_entry_logic.append(dataframe['cti'] < self.entry_38_cti) # Condition #39 - Ichimoku elif index == 39: # Non-Standard protections (add below) # Logic item_entry_logic.append(dataframe['tenkan_sen_1h'] > dataframe['kijun_sen_1h']) item_entry_logic.append(dataframe['close'] > dataframe['cloud_top_1h']) item_entry_logic.append(dataframe['leading_senkou_span_a_1h'] > dataframe['leading_senkou_span_b_1h']) item_entry_logic.append(dataframe['chikou_span_greater_1h']) item_entry_logic.append(dataframe['efi_1h'] > 0) item_entry_logic.append(dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) item_entry_logic.append(dataframe['close'] < dataframe['ssl_up_1h']) item_entry_logic.append(dataframe['cti'] < self.entry_39_cti) item_entry_logic.append(dataframe['r_480'] > self.entry_39_r) item_entry_logic.append(dataframe['r_480_1h'] > self.entry_39_r_1h) item_entry_logic.append(dataframe['rsi_14_1h'] > dataframe['rsi_14_1h'].shift(12)) # Start of trend item_entry_logic.append((dataframe['leading_senkou_span_a_1h'].shift(12) < dataframe['leading_senkou_span_b_1h'].shift(12)) | (dataframe['ssl_up_1h'].shift(12) < dataframe['ssl_down_1h'].shift(12))) # Condition #40 - ZLEMA X entry elif index == 40: # Non-Standard protections (add below) # Logic item_entry_logic.append(qtpylib.crossed_above(dataframe['zlema_2'], dataframe['zlema_4'])) item_entry_logic.append(dataframe['hrsi'] < self.entry_40_hrsi) item_entry_logic.append(dataframe['cci'] < self.entry_40_cci) item_entry_logic.append(dataframe['rsi_14'] < self.entry_40_rsi) item_entry_logic.append(dataframe['cti'] < self.entry_40_cti) item_entry_logic.append(dataframe['r_480'] > self.entry_40_r) item_entry_logic.append(dataframe['r_480_1h'] > self.entry_40_r_1h) # Condition #41 elif index == 41: # Non-Standard protections (add below) # Logic item_entry_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) item_entry_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) item_entry_logic.append(dataframe['cti_1h'] < self.entry_41_cti_1h) item_entry_logic.append(dataframe['r_480_1h'] > self.entry_41_r_1h) item_entry_logic.append(dataframe['close'] < dataframe['sma_75'] * self.entry_41_ma_offset) item_entry_logic.append(dataframe['cti'] < self.entry_41_cti) item_entry_logic.append(dataframe['r_480'] < self.entry_41_r) # Condition #42 elif index == 42: # Non-Standard protections (add below) # Logic item_entry_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) item_entry_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) item_entry_logic.append(dataframe['cti_1h'] < self.entry_42_cti_1h) item_entry_logic.append(dataframe['r_480_1h'] > self.entry_42_r_1h) item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_42_ema_open_mult) item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_42_bb_offset) # Condition #43 elif index == 43: # Non-Standard protections # Logic item_entry_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) item_entry_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) item_entry_logic.append(dataframe['cti_1h'] < self.entry_43_cti_1h) item_entry_logic.append(dataframe['r_480_1h'] > self.entry_43_r_1h) item_entry_logic.append(dataframe['bb40_2_low'].shift().gt(0)) item_entry_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * self.entry_43_bb40_bbdelta_close)) item_entry_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.entry_43_bb40_closedelta_close)) item_entry_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.entry_43_bb40_tail_bbdelta)) item_entry_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift())) item_entry_logic.append(dataframe['close'].le(dataframe['close'].shift())) item_entry_logic.append(dataframe['cti'] < self.entry_43_cti) item_entry_logic.append(dataframe['r_480'] > self.entry_43_r) item_entry_logic.append(dataframe['volume'] > 0) item_entry = reduce(lambda x, y: x & y, item_entry_logic) dataframe.loc[item_entry, 'entry_tag'] += str(index) + ' ' conditions.append(item_entry) if conditions: dataframe.loc[:, 'entry'] = reduce(lambda x, y: x | y, conditions) return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit'] = 0 return dataframe def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, **kwargs) -> bool: """ Called right before placing a regular exit order. Timing for this function is critical, so avoid doing heavy computations or network requests in this method. For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ When not implemented by a strategy, returns True (always confirming). :param pair: Pair that's about to be sold. :param trade: trade object. :param order_type: Order type (as configured in order_types). usually limit or market. :param amount: Amount in quote currency. :param rate: Rate that's going to be used when using limit orders :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled). :param exit_reason: Sell reason. Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss', 'exit_signal', 'force_exit', 'emergency_exit'] :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :return bool: When True is returned, then the exit-order is placed on the exchange. False aborts the process """ if self._should_hold_trade(trade, rate, exit_reason): return False # if self._should_catch_profit_target(pair, trade, rate, exit_reason): # self._set_profit_target(pair, trade, rate, exit_reason) # return False self._remove_profit_target(pair) return True # def _should_catch_profit_target(self, pair: str, trade: "Trade", rate: float, exit_reason: str) -> bool: # if exit_reason == "force_exit": # return False # # ADD LOGIC HERE # if exit_reason in ['profit_target_01']: # return True # return False def _set_profit_target(self, pair: str, rate: float, exit_reason: str): self.custom_info[pair] = {'rate': rate, 'exit_reason': exit_reason} def _remove_profit_target(self, pair: str): if pair in self.custom_info.keys(): self.custom_info.pop(pair) def _should_hold_trade(self, trade: 'Trade', rate: float, exit_reason: str) -> bool: if self.config['runmode'].value in ('live', 'dry_run'): # Just to be sure our hold data is loaded, should be a no-op call after the first bot loop self.load_hold_trades_config() if not self.hold_trade_ids: # We have no pairs we want to hold until profit, exit return False if trade.id not in self.hold_trade_ids: # This pair is not on the list to hold until profit, exit return False trade_profit_ratio = self.hold_trade_ids[trade.id] current_profit_ratio = trade.calc_profit_ratio(rate) if exit_reason == 'force_exit': formatted_profit_ratio = '{}%'.format(trade_profit_ratio * 100) formatted_current_profit_ratio = '{}%'.format(current_profit_ratio * 100) log.warning('Force exiting %s even though the current profit of %s < %s', trade, formatted_current_profit_ratio, formatted_profit_ratio) return False elif current_profit_ratio >= trade_profit_ratio: # This pair is on the list to hold, and we reached minimum profit, exit return False # This pair is on the list to hold, and we haven't reached minimum profit, hold return True else: return False # Elliot Wave Oscillator def ewo(dataframe, sma1_length=5, sma2_length=35): sma1 = ta.EMA(dataframe, timeperiod=sma1_length) sma2 = ta.EMA(dataframe, timeperiod=sma2_length) smadif = (sma1 - sma2) / dataframe['close'] * 100 return smadif # Chaikin Money Flow def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series: """Chaikin Money Flow (CMF) It measures the amount of Money Flow Volume over a specific period. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf Args: dataframe(pandas.Dataframe): dataframe containing ohlcv n(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ mfv = (dataframe['close'] - dataframe['low'] - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low']) mfv = mfv.fillna(0.0) # float division by zero mfv *= dataframe['volume'] cmf = mfv.rolling(n, min_periods=0).sum() / dataframe['volume'].rolling(n, min_periods=0).sum() if fillna: cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0) return Series(cmf, name='cmf') # Williams %R def williams_r(dataframe: DataFrame, period: int=14) -> Series: """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). """ highest_high = dataframe['high'].rolling(center=False, window=period).max() lowest_low = dataframe['low'].rolling(center=False, window=period).min() WR = Series((highest_high - dataframe['close']) / (highest_high - lowest_low), name='{0} Williams %R'.format(period)) return WR * -100 # Volume Weighted Moving Average def vwma(dataframe: DataFrame, length: int=10): """Indicator: Volume Weighted Moving Average (VWMA)""" # Calculate Result pv = dataframe['close'] * dataframe['volume'] vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length)) return vwma # Modified Elder Ray Index def moderi(dataframe: DataFrame, len_slow_ma: int=32) -> Series: slow_ma = Series(ta.EMA(vwma(dataframe, length=len_slow_ma), timeperiod=len_slow_ma)) return slow_ma >= slow_ma.shift(1) # we just need true & false for ERI trend # zlema def zlema(dataframe, timeperiod): lag = int(math.floor((timeperiod - 1) / 2)) if isinstance(dataframe, Series): ema_data = dataframe + (dataframe - dataframe.shift(lag)) else: ema_data = dataframe['close'] + (dataframe['close'] - dataframe['close'].shift(lag)) return ta.EMA(ema_data, timeperiod=timeperiod) # zlhull def zlhull(dataframe, timeperiod): lag = int(math.floor((timeperiod - 1) / 2)) if isinstance(dataframe, Series): wma_data = dataframe + (dataframe - dataframe.shift(lag)) else: wma_data = dataframe['close'] + (dataframe['close'] - dataframe['close'].shift(lag)) return ta.WMA(2 * ta.WMA(wma_data, int(math.floor(timeperiod / 2))) - ta.WMA(wma_data, timeperiod), int(round(np.sqrt(timeperiod)))) # hull def hull(dataframe, timeperiod): if isinstance(dataframe, Series): return ta.WMA(2 * ta.WMA(dataframe, int(math.floor(timeperiod / 2))) - ta.WMA(dataframe, timeperiod), int(round(np.sqrt(timeperiod)))) else: return ta.WMA(2 * ta.WMA(dataframe['close'], int(math.floor(timeperiod / 2))) - ta.WMA(dataframe['close'], timeperiod), int(round(np.sqrt(timeperiod)))) # PMAX def pmax(df, period, multiplier, length, MAtype, src): period = int(period) multiplier = int(multiplier) length = int(length) MAtype = int(MAtype) src = int(src) mavalue = 'MA_' + str(MAtype) + '_' + str(length) atr = 'ATR_' + str(period) pm = 'pm_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype) pmx = 'pmX_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype) # MAtype==1 --> EMA # MAtype==2 --> DEMA # MAtype==3 --> T3 # MAtype==4 --> SMA # MAtype==5 --> VIDYA # MAtype==6 --> TEMA # MAtype==7 --> WMA # MAtype==8 --> VWMA # MAtype==9 --> zema if src == 1: masrc = df['close'] elif src == 2: masrc = (df['high'] + df['low']) / 2 elif src == 3: masrc = (df['high'] + df['low'] + df['close'] + df['open']) / 4 if MAtype == 1: mavalue = ta.EMA(masrc, timeperiod=length) elif MAtype == 2: mavalue = ta.DEMA(masrc, timeperiod=length) elif MAtype == 3: mavalue = ta.T3(masrc, timeperiod=length) elif MAtype == 4: mavalue = ta.SMA(masrc, timeperiod=length) elif MAtype == 5: mavalue = VIDYA(df, length=length) elif MAtype == 6: mavalue = ta.TEMA(masrc, timeperiod=length) elif MAtype == 7: mavalue = ta.WMA(df, timeperiod=length) elif MAtype == 8: mavalue = vwma(df, length) elif MAtype == 9: mavalue = zema(df, period=length) df[atr] = ta.ATR(df, timeperiod=period) df['basic_ub'] = mavalue + multiplier / 10 * df[atr] df['basic_lb'] = mavalue - multiplier / 10 * df[atr] basic_ub = df['basic_ub'].values final_ub = np.full(len(df), 0.0) basic_lb = df['basic_lb'].values final_lb = np.full(len(df), 0.0) for i in range(period, len(df)): final_ub[i] = basic_ub[i] if basic_ub[i] < final_ub[i - 1] or mavalue[i - 1] > final_ub[i - 1] else final_ub[i - 1] final_lb[i] = basic_lb[i] if basic_lb[i] > final_lb[i - 1] or mavalue[i - 1] < final_lb[i - 1] else final_lb[i - 1] df['final_ub'] = final_ub df['final_lb'] = final_lb pm_arr = np.full(len(df), 0.0) for i in range(period, len(df)): pm_arr[i] = final_ub[i] if pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] <= final_ub[i] else final_lb[i] if pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] > final_ub[i] else final_lb[i] if pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] >= final_lb[i] else final_ub[i] if pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] < final_lb[i] else 0.0 pm = Series(pm_arr) # Mark the trend direction up/down pmx = np.where(pm_arr > 0.0, np.where(mavalue < pm_arr, 'down', 'up'), np.NaN) return (pm, pmx) def calc_streaks(series: Series): # logic tables geq = series >= series.shift(1) # True if rising eq = series == series.shift(1) # True if equal logic_table = concat([geq, eq], axis=1) streaks = [0] # holds the streak duration, starts with 0 for row in logic_table.iloc[1:].itertuples(): # iterate through logic table if row[2]: # same value as before streaks.append(0) continue last_value = streaks[-1] if row[1]: # higher value than before streaks.append(last_value + 1 if last_value >= 0 else 1) # increase or reset to +1 else: # lower value than before streaks.append(last_value - 1 if last_value < 0 else -1) # decrease or reset to -1 return streaks # SSL Channels def SSLChannels(dataframe, length=7): df = dataframe.copy() ATR = ta.ATR(dataframe, timeperiod=14) smaHigh = dataframe['high'].rolling(length).mean() + ATR smaLow = dataframe['low'].rolling(length).mean() - ATR hlv = Series(np.where(dataframe['close'] > smaHigh, 1, np.where(dataframe['close'] < smaLow, -1, np.NAN))) hlv = hlv.ffill() sslDown = np.where(hlv < 0, smaHigh, smaLow) sslUp = np.where(hlv < 0, smaLow, smaHigh) return (sslDown, sslUp) #Kalman Filter def KalmanFilter(dtloc, source='close'): dtKF = dtloc.copy().fillna(0) dtKF['TRANGE'] = ta.TRANGE(dtloc).fillna(0) def calc_dtKF(dfr, init=0): global calc_dtKF_value_1 global calc_dtKF_value_2 global calc_dtKF_value_3 global calc_dtKF_source if init == 1: calc_dtKF_value_1 = 0.0 calc_dtKF_value_2 = 0.0 calc_dtKF_value_3 = 0.0 calc_dtKF_source = 0.0 return calc_dtKF_value_1 = 0.2 * (dfr[source] - calc_dtKF_source) + 0.8 * calc_dtKF_value_1 calc_dtKF_value_2 = 0.1 * dfr['TRANGE'] + 0.8 * calc_dtKF_value_2 if calc_dtKF_value_2 != 0: vlambda = abs(calc_dtKF_value_1 / calc_dtKF_value_2) else: vlambda = 0 valpha = (-1 * math.pow(vlambda, 2) + math.sqrt(math.pow(vlambda, 4) + 16 * math.pow(vlambda, 2))) / 8 calc_dtKF_value_3 = valpha * dfr[source] + (1 - valpha) * calc_dtKF_value_3 calc_dtKF_source = dfr[source] return calc_dtKF_value_3 calc_dtKF(None, init=1) dtKF['KF'] = dtKF.apply(calc_dtKF, axis=1) return dtKF['KF'] # ------------------------------ # Utility # ------------------------------ USER_DATA_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) DATA_NFI_TARGET_PROFIT_BY_PAIR_PATH = os.path.join(USER_DATA_DIR, 'data-nfi-profit_target_by_pair.json') def get_profit_target_by_pair() -> Dict: if not os.path.isfile(DATA_NFI_TARGET_PROFIT_BY_PAIR_PATH): return {} f = open(DATA_NFI_TARGET_PROFIT_BY_PAIR_PATH) return json.load(f) def save_profit_target_by_pair(profit_target_by_pair: Dict): file1 = open(DATA_NFI_TARGET_PROFIT_BY_PAIR_PATH, 'w') file1.write(json.dumps(profit_target_by_pair)) file1.close() class Cache: def __init__(self, path): self.path = path self.data = {} self._mtime = None self._previous_data = {} try: self.load() except FileNotFoundError: pass def load(self): if not self._mtime or self.path.stat().st_mtime_ns != self._mtime: self._load() def save(self): if self.data != self._previous_data: self._save() def process_loaded_data(self, data): return data def _load(self): # This method only exists to simplify unit testing with self.path.open('r') as rfh: try: data = json_load(rfh) except rapidjson.JSONDecodeError as exc: log.error('Failed to load JSON from %s: %s', self.path, exc) else: self.data = self.process_loaded_data(data) self._previous_data = copy.deepcopy(self.data) self._mtime = self.path.stat().st_mtime_ns def _save(self): # This method only exists to simplify unit testing file_dump_json(self.path, self.data, is_zip=False, log=True) self._mtime = self.path.stat().st_mtime self._previous_data = copy.deepcopy(self.data) class HoldsCache(Cache): def save(self): raise RuntimeError('The holds cache does not allow programatical save') def process_loaded_data(self, data): trade_ids = data.get('trade_ids') if not trade_ids: return {} rdata = {} open_trades = {trade.id: trade for trade in Trade.get_trades_proxy(is_open=True)} if isinstance(trade_ids, dict): # New syntax for trade_id, profit_ratio in trade_ids.items(): try: trade_id = int(trade_id) except ValueError: log.error("The trade_id(%s) defined under 'trade_ids' in %s is not an integer", trade_id, self.path) continue if not isinstance(profit_ratio, float): log.error("The 'profit_ratio' config value(%s) for trade_id %s in %s is not a float", profit_ratio, trade_id, self.path) if trade_id in open_trades: formatted_profit_ratio = '{}%'.format(profit_ratio * 100) log.warning('The trade %s is configured to HOLD until the profit ratio of %s is met', open_trades[trade_id], formatted_profit_ratio) rdata[trade_id] = profit_ratio else: log.warning("The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s", trade_id, self.path) else: # Initial Syntax profit_ratio = data.get('profit_ratio') if profit_ratio: if not isinstance(profit_ratio, float): log.error("The 'profit_ratio' config value(%s) in %s is not a float", profit_ratio, self.path) else: profit_ratio = 0.005 formatted_profit_ratio = '{}%'.format(profit_ratio * 100) for trade_id in trade_ids: if not isinstance(trade_id, int): log.error("The trade_id(%s) defined under 'trade_ids' in %s is not an integer", trade_id, self.path) continue if trade_id in open_trades: log.warning('The trade %s is configured to HOLD until the profit ratio of %s is met', open_trades[trade_id], formatted_profit_ratio) rdata[trade_id] = profit_ratio else: log.warning("The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s", trade_id, self.path) return rdata